C. F. Rehnborg Professor in Disease Prevention in the School of Medicine and Professor of Health Research and Policy (Epidemiology) and, by courtesy, of Statistics

Medicine - Stanford Prevention Research Center

Bio

Bio

C.F. Rehnborg Chair in Disease Prevention at Stanford University, Professor of Medicine, and of Health Research and Policy at the School of Medicine; Professor of Statistics (by courtesy) at the School of Humanities and Sciences; co-Director, Meta-Research Innovation Center at Stanford; Director of the PhD program in Epidemiology and Clinical Research.

Born in New York City in 1965 and grew up in Athens, Greece. Valedictorian (1984) at Athens College; National Award of the Greek Mathematical Society (1984); graduated (top rank of medical school class) from the University of Athens in 1990; also received a doctorate in biopathology from the same institution. Trained at Harvard and Tufts (internal medicine and infectious diseases), then held positions at NIH, Johns Hopkins and Tufts. Chaired the Department of Hygiene and Epidemiology, University of Ioannina Medical School in 1999-2010 (tenured professor since 2003). Adjunct faculty for Tufts University since 1996 (professor rank since 2002), Director (2008-2010) of the the Center for Genetic Epidemiology and Modeling; also adjunct professor of epidemiology at Harvard School of Public Health and visiting professor of epidemiology and biostatistics at Imperial College. Member of the executive board of the Human Genome Epidemiology Network and Senior Advisor on Knowledge Integration at NCI/NIH (2012-6); served as President, Society for Research Synthesis Methodology, and editorial board member of many leading journals (including PLoS Medicine, Lancet, Annals of Internal Medicine, JNCI, Science Translational Medicine, Clinical Chemistry, Molecular and Cellular Proteomics, AIDS, IJE, JCE, Clinical Trials, and PLoS ONE, among others) and as Editor-in-Chief of the European Journal of Clinical Investigation (2010-now). Delivered over 400 invited and honorary lectures. Recipient of many awards (e.g. European Award for Excellence in Clinical Science [2007], Medal for Distinguished Service, Teachers College, Columbia University [2015]). Inducted in the Association of American Physicians (2009), European Academy of Cancer Sciences (2010) American Epidemiological Society (2015), and European Academy of Sciences and Arts (2015). Honorary titles from the Foundation for Research and Technology-Hellas (FORTH) (2014) and University of Ioannina (2015) and honorary doctorate from Erasmus University Rotterdam (2015). The PLoS Medicine paper on “Why most published research findings are false” has been the most-accessed article in the history of Public Library of Science (~2 million hits). Author of 6 literary books in Greek, two of which (“Toccata for the Girl with the Burnt Face” (Kedros 2012) and “Variations on the Art of the Fugue and a Desperate Ricercar” (Kedros 2014)) were shortlisted for best book of the year Anagnostis awards. Brave Thinker scientist for 2010 according to Atlantic, “may be one of the most influential scientists alive”. Author of >800 papers in peer-reviewed journals, 68% of papers as single/first/last author. Among the most-cited scientists worldwide according to citation databases for which rankings are available (Web of Science/Highly-Cited Researchers, Scopus, Microsoft Academic Search). Among the 50 most-cited scientists across all 20+ million authors publishing across science according to current citation rate (>2,000 new citations per month per Google Scholar, >1000 new citations per month per Scopus or ISI Web of Knowledge). Citation indices: h=148, m=6.7 per Google Scholar (h=120 per ISI and Scopus).

I consider myself privileged to have learned and to continue to learn from interactions with students and young scientists (of all ages) from all over the world and I love to be constantly reminded that I know next to nothing.

Links

Research & Scholarship

Current Research and Scholarly Interests

I have worked in the fields of evidence-based medicine, clinical investigation, clinical and molecular epidemiology, clinical research methodology, empirical research methods, statistics, and genomics. I have a strong interest in meta-research and in large-scale evidence (in particular randomized trials and meta-analyses) and in appraisal and control of diverse biases in biomedical research. I am interested in developing and applying new research methods, and in the interdisciplinary enhancement of existing research methods for study design and analysis in biomedicine. Some of my most influential papers in terms of citations are those addressing issues of replication validity of genetic association studies, biases in biomedical research, research synthesis methods, extensions of meta-analysis, genome-wide association studies and agnostic evaluation of associations, and validity of randomized trials and observational research. I have also designed, steered and participated in influential randomized trials (in particular, the major trials that changed decisively the management and outcome of HIV infection, but also trials in nephrology, and in antibiotic use in the community), and large international consortia that have helped transform the efficiency of research in diverse fields of genomic, molecular and clinical epidemiology. I enjoy working with a diverse array of colleagues from very diverse disciplines and to have an opportunity to learn from both senior and junior investigators, and particularly students at all levels.

Clinical Trials

Personal Genomics for Preventive CardiologyNot Recruiting

The purpose of this study is to see if providing information to a person on their inherited
(genetic) risk of cardiovascular disease (CVD) helps to motivate that person to change their
diet, lifestyle or medication regimen to alter their risk.

Stanford is currently not accepting patients for this trial.For more information, please contact Josh Knowles, 650-804-2526.

Publications

All Publications

Abstract

The language and conceptual framework of "research reproducibility" are nonstandard and unsettled across the sciences. In this Perspective, we review an array of explicit and implicit definitions of reproducibility and related terminology, and discuss how to avoid potential misunderstandings when these terms are used as a surrogate for "truth."

Abstract

There is a growing movement to encourage reproducibility and transparency practices in the scientific community, including public access to raw data and protocols, the conduct of replication studies, systematic integration of evidence in systematic reviews, and the documentation of funding and potential conflicts of interest. In this survey, we assessed the current status of reproducibility and transparency addressing these indicators in a random sample of 441 biomedical journal articles published in 2000-2014. Only one study provided a full protocol and none made all raw data directly available. Replication studies were rare (n = 4), and only 16 studies had their data included in a subsequent systematic review or meta-analysis. The majority of studies did not mention anything about funding or conflicts of interest. The percentage of articles with no statement of conflict decreased substantially between 2000 and 2014 (94.4% in 2000 to 34.6% in 2014); the percentage of articles reporting statements of conflicts (0% in 2000, 15.4% in 2014) or no conflicts (5.6% in 2000, 50.0% in 2014) increased. Articles published in journals in the clinical medicine category versus other fields were almost twice as likely to not include any information on funding and to have private funding. This study provides baseline data to compare future progress in improving these indicators in the scientific literature.

Abstract

As the scientific enterprise has grown in size and diversity, we need empirical evidence on the research process to test and apply interventions that make it more efficient and its results more reliable. Meta-research is an evolving scientific discipline that aims to evaluate and improve research practices. It includes thematic areas of methods, reporting, reproducibility, evaluation, and incentives (how to do, report, verify, correct, and reward science). Much work is already done in this growing field, but efforts to-date are fragmented. We provide a map of ongoing efforts and discuss plans for connecting the multiple meta-research efforts across science worldwide.

Abstract

Several popular screening tests, such as mammography and prostate-specific antigen, have met with wide controversy and/or have lost their endorsement recently. We systematically evaluated evidence from randomized controlled trials (RCTs) as to whether screening decreases mortality from diseases where death is a common outcome.We searched three sources: United States Preventive Services Task Force (USPSTF), Cochrane Database of Systematic Reviews, and PubMed. We extracted recommendation status, category of evidence and RCT availability on mortality for screening tests for diseases on asymptomatic adults (excluding pregnant women and children) from USPSTF. We identified meta-analyses and individual RCTs on screening and mortality from Cochrane and PubMed.We selected 19 diseases (39 tests) out of 50 diseases/disorders for which USPSTF provides screening evaluation. Screening is recommended for 6 diseases (12 tests) out of the 19. We assessed 9 non-overlapping meta-analyses and 48 individual trials for these 19 diseases. Among the results of the meta-analyses, reductions where the 95% confidence intervals (CIs) excluded the null occurred for four disease-specific mortality estimates (ultrasound for abdominal aortic aneurysm in men; mammography for breast cancer; fecal occult blood test and flexible sigmoidoscopy for colorectal cancer) and for none of the all-cause mortality estimates. Among individual RCTs, reductions in disease-specific and all-cause mortality where the 95% CIs excluded the null occurred in 30% and 11% of the estimates, respectively.Among currently available screening tests for diseases where death is a common outcome, reductions in disease-specific mortality are uncommon and reductions in all-cause mortality are very rare or non-existent.

Abstract

Citation metrics are increasingly used to appraise published research. One challenge is whether and how to normalize these metrics to account for differences across scientific fields, age (year of publication), type of document, database coverage, and other factors. We discuss the pros and cons for normalizations using different approaches. Additional challenges emerge when citation metrics need to be combined across multiple papers to appraise the corpus of scientists, institutions, journals, or countries, as well as when trying to attribute credit in multiauthored papers. Different citation metrics may offer complementary insights, but one should carefully consider the assumptions that underlie their calculation.

Abstract

To evaluate the research agenda of registered randomized trials comparing generic and brand-name drugs in terms of who sponsors them, whether they are published promptly, and whether they find favorable results.We included randomized trials comparing the safety or efficacy of brand-name vs generic medications that were registered in ClinicalTrials.gov or other registries from January 1, 2000, through July 31, 2015. To identify published articles or results generated from such trials, we searched PubMed, Scopus, Google, and registry databases. Data were compared across sponsorship categories ("inbred" if the compared drugs were owned by the same company or its partners/subsidiaries, "competitive" if the compared drugs were owned by competing companies, and "apparently nonprofit"), and time to publication was evaluated with Cox analysis.We found 207 registered protocols reporting on 186 completed trials. Among those trials, 37 had published their results and another 56 had posted results in registries, for a total of 93 trials with available results. Four years after trial completion, results were available for 64 of 138 trials (46.4%), with substantial differences by sponsor: 70.8% (34 of 48), 28.1% (18 of 64), and 46.2% (12 of 26) of the inbred, competitive, and nonprofit trials, respectively. In multivariate modeling, inbred trials had a 1.73-fold risk of having results available compared with competitive trials (P=.04). Almost all trials reported favorable results, with the exception of 4 (4.3% of the 93 trials with results).Despite the importance of generic drugs, relatively few registered randomized trials have compared the health effects of generic vs brand-name medicines, and there is an associated unsatisfactory publication rate and almost ubiquitous favorable results. The overall literature on the topic is at high risk of bias, possibly in favor of generic drugs. Higher nonprofit funding and stronger pressure to register trials and publish results are needed.

Abstract

Many fields face an increasing prevalence of multi-authorship, and this poses challenges in assessing citation metrics. Here, we explore multiple citation indicators that address total impact (number of citations, Hirsch H index [H]), co-authorship adjustment (Schreiber Hm index [Hm]), and author order (total citations to papers as single; single or first; or single, first, or last author). We demonstrate the correlation patterns between these indicators across 84,116 scientists (those among the top 30,000 for impact in a single year [2013] in at least one of these indicators) and separately across 12 scientific fields. Correlation patterns vary across these 12 fields. In physics, total citations are highly negatively correlated with indicators of co-authorship adjustment and of author order, while in other sciences the negative correlation is seen only for total citation impact and citations to papers as single author. We propose a composite score that sums standardized values of these six log-transformed indicators. Of the 1,000 top-ranked scientists with the composite score, only 322 are in the top 1,000 based on total citations. Many Nobel laureates and other extremely influential scientists rank among the top-1,000 with the composite indicator, but would rank much lower based on total citations. Conversely, many of the top 1,000 authors on total citations have had no single/first/last-authored cited paper. More Nobel laureates of 2011-2015 are among the top authors when authors are ranked by the composite score than by total citations, H index, or Hm index; 40/47 of these laureates are among the top 30,000 by at least one of the six indicators. We also explore the sensitivity of indicators to self-citation and alphabetic ordering of authors in papers across different scientific fields. Multiple indicators and their composite may give a more comprehensive picture of impact, although no citation indicator, single or composite, can be expected to select all the best scientists.

Abstract

Expression of various long noncoding RNAs (lncRNAs) may affect cancer prognosis. Here, we aim to gather and examine all evidence on the potential role of lncRNAs as novel predictors of survival in human cancer.We systematically searched through PubMed, to identify all published studies reporting on the association between any individual lncRNA or group of lncRNAs with prognosis in human cancer (death or other clinical outcomes). Where appropriate, we then performed quantitative synthesis of those results using meta-analytic methods to identify the true effect size of lncRNAs on cancer prognosis. The reliability of those results was then examined using measures of heterogeneity and testing for selective reporting biases.Three hundred ninety-two studies were screened to eventually identify 111 eligible studies on 127 datasets. In total, these represented 16,754 independent participants pertaining to 53 individual and 6 grouped lncRNAs within a total of 19 cancer sites. Overall, 83 % of the studies we identified addressed overall survival and 32 % of the studies addressed recurrence-free survival. For overall survival, 96 % (88/92) of studies identified a statistically significant association of lncRNA expression to prognosis. Meta-analysis of 6 out of 7 lncRNAs for which three or more studies were available, identified statistically significant associations with overall survival. The lncRNA HOTAIR was by far the most broadly studied lncRNA (n = 29; of 111 studies) and featured a summary hazard ratio (HR) of 2.22 (95 % confidence interval (CI), 1.86-2.65) with modest heterogeneity (I(2) = 49 %; 95 % CI, 14-79 %). Prominent excess significance was demonstrated across all meta-analyses (p-value = 0.0003), raising the possibility of substantial selective reporting biases.Multiple lncRNAs have been shown to be strongly associated with prognosis in diverse cancers, but substantial bias cannot be excluded in this field and larger studies are needed to understand whether these prognostic information may eventually be useful.

Abstract

Nonnucleoside reverse-transcriptase inhibitor (NNRTI)-associated transmitted drug resistance (TDR) is the most common type of TDR. Few data guide the selection of antiretroviral therapy (ART) for patients with such resistance.We reviewed treatment outcomes in a cohort of HIV-1-infected patients with isolated NNRTI TDR who initiated ART between April 2002 and May 2014. In an as-treated analysis, virological failure (VF) was defined as not reaching undetectable virus levels within 24 weeks, virological rebound, or switching regimens during viremia. In an intention-to-treat (ITT) analysis, failure was defined more broadly as VF, loss to follow-up (LTFU), and switching during virological suppression.Of 3,245 patients, 131 (4.0%) had isolated NNRTI TDR. 122 received a standard regimen comprising two NRTIs plus a boosted protease inhibitor (bPI; n=54), an integrase strand transfer inhibitor (INSTI; n=52), or an NNRTI (n=16). The median follow-up was 100 weeks. In the as-treated analysis, VF occurred in 15% (n=8), 2% (n=1) and 25% (n=4) of patients in the bPI, INSTI and NNRTI groups, respectively. In multivariate regression, there was a trend toward a lower risk of VF with INSTIs than with bPIs (HR 0.14; 95% CI, 0.02,1.1; p = 0.07). In ITT multivariate regression, INSTIs had a lower risk of failure than bPIs (HR 0.38; 95% CI, 0.18,0.82; p = 0.01).Patients with isolated NNRTI TDR experienced low VF rates with INSTIs and bPIs. INSTIs were non-inferior to bPIs in an analysis of VF but superior to bPIs when frequency of switching and LTFU were also considered.

Abstract

Elderly patients represent the greatest consumers of healthcare per capita but have historically been underrepresented in clinical trials. It is unknown how many trials are designed to focus exclusively on elderly patients.To define the prevalence of interventional trials that study exclusively elderly persons and describe the characteristics of these trials, including their distribution across conditions most prevalent in the elderly.All interventional clinical trials enrolling exclusively elderly patients (≥65 years), conducted primarily in high-income countries, and initiated between 2006 and 2014, identified through ClincialTrials.gov.Trials were identified and characterized according to design features and disease categories studied. Across disease categories we examined the burden of disease in the elderly in high-income countries (measured in disability-adjusted life years [DALYs]) and compared to the number of trials conducted exclusively in the elderly.Among 80,965 interventional trials, 1,112 (1.4%) focused on elderly patients. Diverse types of interventions were studied in these trials (medications 33%, behavioral interventions 18%, and dietary supplements 10%) and the majority was funded by non-profit organizations (81%). Studies tended to be small (median sample size 122 participants [IQR 58, 305]), single-center studies (67%). Only 43% of 126 disease categories affecting elderly persons were studied in trials focused on the elderly. Among these disease categories, there was a 5162-fold range in the ratio of DALYs per trial. Across 5 conditions where over 80% of DALYs are in the elderly, there were a total of only 117 trials done exclusively in the elderly.Very few and mostly small studies are conducted exclusively in elderly persons, even for conditions that affect almost exclusively the elderly.

Abstract

Studies that use routinely collected health data (RCD studies) are advocated to complement evidence from randomized controlled trials (RCTs) for comparative effectiveness research and to inform health care decisions when RCTs would be unfeasible. We aimed to evaluate the current use of routinely collected health data to complement RCT evidence.We searched PubMed for RCD studies published to 2010 that evaluated the comparative effectiveness of medical treatments on mortality using propensity scores. We identified RCTs of the same treatment comparisons and evaluated how frequently the RCD studies analyzed treatments that had not been compared previously in randomized trials. When RCTs did exist, we noted the claimed motivations for each RCD study. We also analyzed the citation impact of the RCD studies.Of 337 eligible RCD studies identified, 231 (68.5%) analyzed treatments that had already been compared in RCTs. The study investigators rarely claimed that it would be unethical (6/337) or difficult (18/337) to perform RCTs on the same question. Evidence from RCTs was mentioned or cited by authors of 213 RCD studies. The most common motivations for conducting the RCD studies were alleged limited generalizability of trial results to the "real world" (37.6%), evaluation of specific outcomes (31.9%) or specific populations (23.5%), and inconclusive or inconsistent evidence from randomized trials (25.8%). Studies evaluating "real world" effects had the lowest citation impact.Most of the RCD studies we identified explored comparative treatment effects that had already been investigated in RCTs. The objective of such studies needs to shift more toward answering pivotal questions that are not supported by trial evidence or for which RCTs would be unfeasible.

Abstract

There is debate whether clinical trials with suboptimal power are justified and whether results from large studies are more reliable than the (combined) results of smaller trials. We quantified the error rates for evaluations based on single conventionally powered trials (80% or 90% power) versus evaluations based on the random-effects meta-analysis of a series of smaller trials. When a treatment was assumed to have no effect but heterogeneity was present, the error rates for a single trial were increased more than 10-fold above the nominal rate, even for low heterogeneity. Conversely, for meta-analyses on a series of trials, the error rates were correct. When selective publication was present, the error rates were always increased, but they still tended to be lower for a series of trials than single trials. We conclude that evidence of efficacy based on a series of (smaller) trials, may lower the error rates compared with using a single well-powered trial. Only when both heterogeneity and selective publication can be excluded, a single trial is able to provide conclusive evidence.

Abstract

Instead of evaluating one risk factor at a time, we illustrate the utility of "field-wide meta-analyses" in considering all available data on all putative risk factors of a disease simultaneously.We identified studies on putative risk factors of pterygium (surfer's eye) in PubMed, EMBASE, and Web of Science. We mapped which factors were considered, reported, and adjusted for in each study. For each putative risk factor, four meta-analyses were done using univariate only, multivariate only, preferentially univariate, or preferentially multivariate estimates.A total of 2052 records were screened to identify 60 eligible studies reporting on 65 putative risk factors. Only 4 of 60 studies reported both multivariate and univariate regression analyses. None of the 32 studies using multivariate analysis adjusted for the same set of risk factors. Effect sizes from different types of regression analyses led to significantly different summary effect sizes (P-value

Abstract

The p-curve, the distribution of statistically significant p-values of published studies, has been used to make inferences on the proportion of true effects and on the presence of p-hacking in the published literature. We analyze the p-curve for observational research in the presence of p-hacking. We show by means of simulations that even with minimal omitted-variable bias (e.g., unaccounted confounding) p-curves based on true effects and p-curves based on null-effects with p-hacking cannot be reliably distinguished. We also demonstrate this problem using as practical example the evaluation of the effect of malaria prevalence on economic growth between 1960 and 1996. These findings call recent studies into question that use the p-curve to infer that most published research findings are based on true effects in the medical literature and in a wide range of disciplines. p-values in observational research may need to be empirically calibrated to be interpretable with respect to the commonly used significance threshold of 0.05. Violations of randomization in experimental studies may also result in situations where the use of p-curves is similarly unreliable.

Abstract

To assess differences in estimated treatment effects for mortality between observational studies with routinely collected health data (RCD; that are published before trials are available) and subsequent evidence from randomized controlled trials on the same clinical question. Meta-epidemiological survey. PubMed searched up to November 2014. Eligible RCD studies were published up to 2010 that used propensity scores to address confounding bias and reported comparative effects of interventions for mortality. The analysis included only RCD studies conducted before any trial was published on the same topic. The direction of treatment effects, confidence intervals, and effect sizes (odds ratios) were compared between RCD studies and randomized controlled trials. The relative odds ratio (that is, the summary odds ratio of trial(s) divided by the RCD study estimate) and the summary relative odds ratio were calculated across all pairs of RCD studies and trials. A summary relative odds ratio greater than one indicates that RCD studies gave more favorable mortality results. The evaluation included 16 eligible RCD studies, and 36 subsequent published randomized controlled trials investigating the same clinical questions (with 17 275 patients and 835 deaths). Trials were published a median of three years after the corresponding RCD study. For five (31%) of the 16 clinical questions, the direction of treatment effects differed between RCD studies and trials. Confidence intervals in nine (56%) RCD studies did not include the RCT effect estimate. Overall, RCD studies showed significantly more favorable mortality estimates by 31% than subsequent trials (summary relative odds ratio 1.31 (95% confidence interval 1.03 to 1.65; I(2)=0%)). Studies of routinely collected health data could give different answers from subsequent randomized controlled trials on the same clinical questions, and may substantially overestimate treatment effects. Caution is needed to prevent misguided clinical decision making.

Abstract

For any health intervention, accurate knowledge of both benefits and harms is needed. Systematic reviews often compound poor reporting of harms in primary studies by failing to report harms or doing so inadequately. While the PRISMA statement (Preferred Reporting Items for Systematic reviews and Meta-Analyses) helps systematic review authors ensure complete and transparent reporting, it is focused mainly on efficacy. Thus, a PRISMA harms checklist has been developed to improve harms reporting in systematic reviews, promoting a more balanced assessment of benefits and harms. A development strategy, endorsed by the EQUATOR Network and existing reporting guidelines (including the PRISMA statement, PRISMA for abstracts, and PRISMA for protocols), was used. After the development of a draft checklist of items, a modified Delphi process was initiated. The Delphi consisted of three rounds of electronic feedback followed by an in-person meeting. The PRISMA harms checklist contains four essential reporting elements to be added to the original PRISMA statement to improve harms reporting in reviews. These are reported in the title ("Specifically mention 'harms' or other related terms, or the harm of interest in the review"), synthesis of results ("Specify how zero events were handled, if relevant"), study characteristics ("Define each harm addressed, how it was ascertained (eg, patient report, active search), and over what time period"), and synthesis of results ("Describe any assessment of possible causality"). Additional guidance regarding existing PRISMA items was developed to demonstrate relevance when synthesising information about harms. The PRISMA harms checklist identifies a minimal set of items to be reported when reviewing adverse events. This guideline extension is intended to improve harms reporting in systematic reviews, whether harms are a primary or secondary outcome.

Abstract

To identify the impact of industry involvement in the publication and interpretation of meta-analyses of antidepressant trials in depression.Using MEDLINE, we identified all meta-analyses evaluating antidepressants for depression published in January 2007-March 2014. We extracted data pertaining to author affiliations, conflicts of interest, and whether the conclusion of the abstract included negative statements on whether the antidepressant(s) were effective or safe.We identified 185 eligible meta-analyses. Fifty-four meta-analyses (29%) had authors who were employees of the assessed drug manufacturer, and 147 (79%) had some industry link (sponsorship or authors who were industry employees and/or had conflicts of interest). Only 58 meta-analyses (31%) had negative statements in the concluding statement of the abstract. Meta-analyses including an author who were employees of the manufacturer of the assessed drug were 22-fold less likely to have negative statements about the drug than other meta-analyses [1/54 (2%) vs. 57/131 (44%); P

Abstract

Parkinson's disease is a neurological disorder with complex pathogenesis implicating both environmental and genetic factors. We aimed to summarise the environmental risk factors that have been studied for potential association with Parkinson's disease, assess the presence of diverse biases, and identify the risk factors with the strongest support.We searched PubMed from inception to September 18, 2015, to identify systematic reviews and meta-analyses of observational studies that examined associations between environmental factors and Parkinson's disease. For each meta-analysis we estimated the summary effect size by random-effects and fixed-effects models, the 95% confidence interval and the 95% prediction interval. We estimated the between-study heterogeneity expressed by I(2), evidence of small-study effects and evidence of excess significance bias.Overall, 75 unique meta-analyses on different risk factors for Parkinson's disease were examined, covering diverse biomarkers, dietary factors, drugs, medical history or comorbid diseases, exposure to toxic environmental agents and habits. 21 of 75 meta-analyses had results that were significant at p

Abstract

The study aims to assess the status of registration of observational studies.We identified studies on cancer research with prospective recruitment of participants that were registered from February 2000 to December 2011 in ClinicalTrials.gov. We recorded the dates of registration and start of recruitment, outcomes, and description of statistical method. We searched for publications corresponding to the registered studies through May 31, 2014.One thousand one hundred nine registered studies were eligible. Primary and secondary outcomes were reported in 809 (73.0%) and 464 (41.8%) of them. The date of registration preceded the month of the study start in 145 (13.8%) and coincided in 205 (19.5%). A total of 151 publications from 120 (10.8%) registered studies were identified. In 2 (33.3%) of the 6 publications where ClinicalTrials.gov reported that the study started recruitment after registration, and in 9 (50.0%) of 18 publications where ClinicalTrials.gov reported the same date for registration and start of recruitment, the articles showed that the study had actually started recruiting before registration.During the period reviewed, few observational studies have been registered. Registration usually occurred after the study started, and prespecification of outcomes and statistical analysis rarely occurred.

Abstract

During January 2015, President Obama announced the Precision Medicine Initiative [1], strengthening communal efforts to integrate patient-centric molecular, environmental, and clinical "big" data. Such efforts have already improved aspects of clinical management for diseases such as non-small cell lung carcinoma [2], breast cancer [3], and hypertrophic cardiomyopathy [4]. To maintain this track record, it is necessary to cultivate practices that ensure reproducibility as large-scale heterogeneous datasets and databases proliferate. For example, the NIH has outlined initiatives to enhance reproducibility in preclinical research [5], both Science [6] and Nature [7] have featured recent editorials on reproducibility, and several authors have noted the issues of utilizing big data for public health [8], but few methods exist to ensure that big data resources motivated by precision medicine are being used reproducibly. Relevant challenges include: (1) integrative analyses of heterogeneous measurement platforms (e.g. genomic, clinical, quantified self, and exposure data), (2) the tradeoff in making personalized decisions using more targeted (e.g. individual-level) but potentially much noisier subsets of data, and (3) the unprecedented scale of asynchronous observational and population level inquiry (i.e. many investigators separately mining shared/publicly-available data)….

Abstract

It is a public health priority to identify the adverse and non-adverse associations between pharmaceutical medications and cancer. We search for and evaluate associations between all prescribed medications and longitudinal cancer risk in participants of the Swedish Cancer Register (N = 9,014,975). We associated 552 different medications with incident cancer risk (any, breast, colon, and prostate) during 5.5 years of follow-up (7/1/2005-12/31/2010) in two types of statistical models, time-to-event and case-crossover. After multiple hypotheses correction and replication, 141 (26%) drugs were associated with any cancer in a time-to-event analysis constraining drug exposure to 1 year before first cancer diagnosis and adjusting for history of medication use. In a case-crossover analysis, 36 drugs (7%) were associated with decreased cancer risk. 12 drugs were found in common in both analyses with concordant direction of association. We found 14, 10, 7% of all drugs associated with colon, prostate, and breast cancers in time-to-event models. We only found 1, 2%, and 0% for these cancers, respectively, in case-crossover analyses. Pharmacoepidemiologic analyses of cancer risk are sensitive to modeling choices and false-positive findings are a threat. Medication-wide analyses using different analytical models may help suggest consistent signals of increased cancer risk.

Abstract

The Depression subscale of the Hospital Anxiety and Depression Scale (HADS-D) has been recommended for depression screening in medically ill patients. Many existing HADS-D studies have used exploratory methods to select optimal cut-offs. Often, these studies report results from a small range of cut-off thresholds; cut-offs with more favourable accuracy results are more likely to be reported than others with worse accuracy estimates. When published data are combined in meta-analyses, selective reporting may generate biased summary estimates. Individual patient data (IPD) meta-analyses can address this problem by estimating accuracy with data from all studies for all relevant cut-off scores. In addition, a predictive algorithm can be generated to estimate the probability that a patient has depression based on a HADS-D score and clinical characteristics rather than dichotomous screening classification alone. The primary objectives of our IPD meta-analyses are to determine the diagnostic accuracy of the HADS-D to detect major depression among adults across all potentially relevant cut-off scores and to generate a predictive algorithm for individual patients. We are already aware of over 100 eligible studies, and more may be identified with our comprehensive search.Data sources will include MEDLINE, MEDLINE In-Process & Other Non-Indexed Citations, PsycINFO and Web of Science. Eligible studies will have datasets where patients are assessed for major depression based on a validated structured or semistructured clinical interview and complete the HADS-D within 2 weeks (before or after). Risk of bias will be assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Bivariate random-effects meta-analysis will be conducted for the full range of plausible cut-off values, and a predictive algorithm for individual patients will be generated.The findings of this study will be of interest to stakeholders involved in research, clinical practice and policy.

Abstract

It has been argued that incentive systems should be multi-dimensional, including productivity, quality, reproducibility, sharing, and translation potential ("PQRST"),(1) but many current systems weight productivity particularly heavily. These systems directly affect the volume, and indirectly the quality of the scientific publication record. This was recognized at least as far back as the 1980s, with a proposal that promotion committees consider only a handful of a scientist's publications, in the hopes of improving the quality of our "large and largely trivial" literature. This article is protected by copyright. All rights reserved.

Abstract

Accurate diagnosis and early detection of complex diseases, such as Parkinson's disease, has the potential to be of great benefit for researchers and clinical practice. We aimed to create a non-invasive, accurate classification model for the diagnosis of Parkinson's disease, which could serve as a basis for future disease prediction studies in longitudinal cohorts.We developed a model for disease classification using data from the Parkinson's Progression Marker Initiative (PPMI) study for 367 patients with Parkinson's disease and phenotypically typical imaging data and 165 controls without neurological disease. Olfactory function, genetic risk, family history of Parkinson's disease, age, and gender were algorithmically selected by stepwise logistic regression as significant contributors to our classifying model. We then tested the model with data from 825 patients with Parkinson's disease and 261 controls from five independent cohorts with varying recruitment strategies and designs: the Parkinson's Disease Biomarkers Program (PDBP), the Parkinson's Associated Risk Study (PARS), 23andMe, the Longitudinal and Biomarker Study in PD (LABS-PD), and the Morris K Udall Parkinson's Disease Research Center of Excellence cohort (Penn-Udall). Additionally, we used our model to investigate patients who had imaging scans without evidence of dopaminergic deficit (SWEDD).In the population from PPMI, our initial model correctly distinguished patients with Parkinson's disease from controls at an area under the curve (AUC) of 0·923 (95% CI 0·900-0·946) with high sensitivity (0·834, 95% CI 0·711-0·883) and specificity (0·903, 95% CI 0·824-0·946) at its optimum AUC threshold (0·655). All Hosmer-Lemeshow simulations suggested that when parsed into random subgroups, the subgroup data matched that of the overall cohort. External validation showed good classification of Parkinson's disease, with AUCs of 0·894 (95% CI 0·867-0·921) in the PDBP cohort, 0·998 (0·992-1·000) in PARS, 0·955 (no 95% CI available) in 23andMe, 0·929 (0·896-0·962) in LABS-PD, and 0·939 (0·891-0·986) in the Penn-Udall cohort. Four of 17 SWEDD participants who our model classified as having Parkinson's disease converted to Parkinson's disease within 1 year, whereas only one of 38 SWEDD participants who were not classified as having Parkinson's disease underwent conversion (test of proportions, p=0·003).Our model provides a potential new approach to distinguish participants with Parkinson's disease from controls. If the model can also identify individuals with prodromal or preclinical Parkinson's disease in prospective cohorts, it could facilitate identification of biomarkers and interventions.National Institute on Aging, National Institute of Neurological Disorders and Stroke, and the Michael J Fox Foundation.

Abstract

Between-study heterogeneity plays an important role in random-effects models for meta-analysis. Most clinical trials are small, and small trials are often associated with larger effect sizes. We empirically evaluated whether there is also a relationship between trial size and heterogeneity (τ).We selected the first meta-analysis per intervention review of the Cochrane Database of Systematic Reviews Issues 2009-2013 with a dichotomous (n = 2,009) or continuous (n = 1,254) outcome. The association between estimated τ and trial size was evaluated across meta-analyses using regression and within meta-analyses using a Bayesian approach. Small trials were predefined as those having standard errors (SEs) over 0.2 standardized effects.Most meta-analyses were based on few (median 4) trials. Within the same meta-analysis, the small study τS(2) was larger than the large-study τL(2) [average ratio 2.11; 95% credible interval (1.05, 3.87) for dichotomous and 3.11 (2.00, 4.78) for continuous meta-analyses]. The imprecision of τS was larger than of τL: median SE 0.39 vs. 0.20 for dichotomous and 0.22 vs. 0.13 for continuous small-study and large-study meta-analyses.Heterogeneity between small studies is larger than between larger studies. The large imprecision with which τ is estimated in a typical small-studies' meta-analysis is another reason for concern, and sensitivity analyses are recommended.

Abstract

The PRISMA statement is a reporting guideline designed to improve the completeness of reporting of systematic reviews and meta-analyses. Authors have used this guideline worldwide to prepare their reviews for publication. In the past, these reports typically compared 2 treatment alternatives. With the evolution of systematic reviews that compare multiple treatments, some of them only indirectly, authors face novel challenges for conducting and reporting their reviews. This extension of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) statement was developed specifically to improve the reporting of systematic reviews incorporating network meta-analyses. A group of experts participated in a systematic review, Delphi survey, and face-to-face discussion and consensus meeting to establish new checklist items for this extension statement. Current PRISMA items were also clarified. A modified, 32-item PRISMA extension checklist was developed to address what the group considered to be immediately relevant to the reporting of network meta-analyses. This document presents the extension and provides examples of good reporting, as well as elaborations regarding the rationale for new checklist items and the modification of previously existing items from the PRISMA statement. It also highlights educational information related to key considerations in the practice of network meta-analysis. The target audience includes authors and readers of network meta-analyses, as well as journal editors and peer reviewers.

Abstract

To understand the translational trajectory of genomic tests in cancer screening, diagnosis, prognosis, and treatment, we reviewed tests that have been assessed by recommendation and guideline developers.For each test, we marked translational milestones by determining when the genomic association with cancer was first discovered and studied in patients, and when a health application for a specified clinical use was successfully demonstrated and approved or cleared by the US Food and Drug Administration. To identify recommendations and guidelines, we reviewed the websites of cancer, genomic, and general guideline developers and professional organizations. We searched the in vitro diagnostics database of the US Food and Drug Administration for information, and we searched PubMed for translational milestones. Milestones were examined against type of recommendation, Food and Drug Administration approval or clearance, disease rarity, and test purpose.Of the 45 tests we identified, 9 received strong recommendations for their usage in clinical settings, 14 received positive but moderate recommendations, and 22 were not currently recommended. For 18 tests, two or more different sources had issued recommendations, with 67% concordance. Only five tests had Food and Drug Administration approval, and an additional five had clearance. The median time from discovery to recommendation statement was 14.7 years.In general, there were no associations found between translational trajectory and recommendation category.Genet Med 17 6, 431-440.

Abstract

Proteome analysis is increasingly being used in investigations elucidating the molecular basis of disease, identifying diagnostic and prognostic markers, and ultimately improving patient care. We appraised the current status of proteomic investigations using human samples, including the state of the art in proteomic technologies, from sample preparation to data evaluation approaches, as well as key epidemiologic, statistical, and translational issues. We systematically reviewed the most highly cited clinical proteomic studies published between January 2009 and March 2014 that included a minimum of 100 samples, as well as strategies that have been successfully implemented to enhance the translational relevance of proteomic investigations. Limited comparability between studies and lack of specification of biomarker context of use are frequently observed. Nevertheless, there are initial examples of successful biomarker discovery in cross-sectional studies followed by validation in high-risk longitudinal cohorts. Translational potential is currently hindered, as limitations in proteomic investigations are not accounted for. Interdisciplinary communication between proteomics experts, basic researchers, epidemiologists, and clinicians, an orchestrated assimilation of required resources, and a more systematic translational outlook for accumulation of evidence may augment the public health impact of proteomic investigations.

Abstract

We updated a field synopsis of genetic associations of cutaneous melanoma (CM) by systematically retrieving and combining data from all studies in the field published as of August 31, 2013. Data were available from 197 studies, which included 83,343 CM cases and 187,809 controls and reported on 1,126 polymorphisms in 289 different genes. Random-effects meta-analyses of 81 eligible polymorphisms evaluated in >4 data sets confirmed 20 single-nucleotide polymorphisms across 10 loci (TYR, AFG3L1P, CDK10, MYH7B, SLC45A2, MTAP, ATM, CLPTM1L, FTO, and CASP8) that have previously been published with genome-wide significant evidence for association (P<5 × 10(-8)) with CM risk, with certain variants possibly functioning as proxies of already tagged genes. Four other loci (MITF, CCND1, MX2, and PLA2G6) were also significantly associated with 5 × 10(-8)

Abstract

The cause of multiple sclerosis is believed to involve environmental exposure and genetic susceptibility. We aimed to summarise the environmental risk factors that have been studied in relation to onset of multiple sclerosis, assess whether there is evidence for diverse biases in this literature, and identify risk factors without evidence of biases.We searched PubMed from inception to Nov 22, 2014, to identify systematic reviews and meta-analyses of observational studies that examined associations between environmental factors and multiple sclerosis. For each meta-analysis we estimated the summary effect size by use of random-effects and fixed-effects models, the 95% CI, and the 95% prediction interval. We estimated the between-study heterogeneity expressed by I(2) (defined as large for I(2)≥50%), evidence of small-study effects (ie, large studies had significantly more conservative results than smaller studies), and evidence of excess significance bias (ie, more studies than expected with significant results).Overall, 44 unique meta-analyses including 416 primary studies of different risk factors and multiple sclerosis were examined, covering a wide range of risk factors: vaccinations, comorbid diseases, surgeries, traumatic events and accidents, exposure to environmental agents, and biochemical, infectious, and musculoskeletal biomarkers. 23 of 44 meta-analyses had results that were significant at p values less than 0·05 and 11 at p values less than 0·001 under the random-effects model. Only three of the 11 significant meta-analyses (p<0·001) included more than 1000 cases, had 95% prediction intervals excluding the null value, and were not suggestive of large heterogeneity (I(2)<50%), small-study effects (p for Egger's test >0·10), or excess significance (p>0·05). These were IgG seropositivity to Epstein-Barr virus nuclear antigen (EBNA) (random effects odds ratio [OR] 4·46, 95% CI 3·26-6·09; p for effect size=1·5 × 10(-19); I(2)=43%), infectious mononucleosis (2·17, 1·97-2·39; p=3·1 × 10(-50); I(2)=0%), and smoking (1·52, 1·39-1·66; p=1·7 × 10(-18;)I(2)=0%).Many studies on environmental factors associated with multiple sclerosis have caveats casting doubts on their validity. Data from more and better-designed studies are needed to establish robust evidence. A biomarker of Epstein-Barr virus (anti-EBNA IgG seropositivity), infectious mononucleosis, and smoking showed the strongest consistent evidence of an association.None.

Abstract

A fuller understanding of the social epidemiology of disease requires an extended description of the relationships between social factors and health indicators in a systematic manner. In the present study, we investigated the correlations between income and 330 indicators of physiological, biochemical, and environmental health in participants in the US National Health and Nutrition Examination Survey (NHANES) (1999-2006). We combined data from 3 survey waves (n = 249-23,649 for various indicators) to search for linear and nonlinear (quadratic) correlates of income, and we validated significant (P < 0.00015) correlations in an independent testing data set (n = 255-7,855). We validated 66 out of 330 factors, including infectious (e.g., hepatitis A), biochemical (e.g., carotenoids, high-density lipoprotein cholesterol), physiological (e.g., upper leg length), and environmental (e.g., lead, cotinine) measures. We found only a modest amount of association modification by age, race/ethnicity, and gender, and there was no association modification for blacks. The present study is descriptive, not causal. We have shown in our systematic investigation the crucial place income has in relation to health risk factors. Future research can use these correlations to better inform theory and studies of pathways to disease, as well as utilize these findings to understand when confounding by income is most likely to introduce bias.

Abstract

Meta-analyses of biomarkers often present spurious significant results and large effects. We applied sensitivity analyses with the use of credibility ceilings to assess whether and how the results of meta-analyses of biomarkers and cancer risk would change.We evaluated 98 meta-analyses, 43 (44%) of which had nominally statistically significant results. We assumed that any single study cannot give more than a maximum certainty 100 - c% (c, credibility ceiling) that the effect estimate [odds ratio (OR)] exceeds 1 (null) or 1.2.Nominal statistical significance was maintained for 21 (21%) meta-analyses, for c = 10% and OR >1, and these proportions changed to 7%, 3%, and 6% with ceilings of 20%, 30%, and 40%, respectively. For ceilings for OR >1.2, the respective proportions were 37%, 21%, 7%, and 3%. Seven meta-analyses on infectious agents retained statistical significance even with a high ceiling of c = 20% for OR >1.00. Meta-analyses without other hints of bias (large between-study heterogeneity, small-study effects, excess significance) were more likely to retain statistical significance than those that had such hints of bias.Credibility ceilings may be helpful in meta-analyses of biomarkers to understand the robustness of the results to different levels of uncertainty.

Abstract

To map the availability of information on a major clinical outcome--chronic lung disease--across the randomized controlled trials in systematic reviews of an entire specialty, specifically interventions in preterm infants.Survey of systematic reviews.Cochrane Database of Systematic Reviews.All Cochrane systematic reviews (as of November 2013) that had evaluated interventions in preterm infants. We identified how many of those systematic reviews had looked for information on chronic lung disease, how many reported on chronic lung disease, and how many of the randomized controlled trials included in the systematic reviews reported on chronic lung disease. We also randomly selected 10 systematic reviews that did not report on chronic lung disease and 10 that reported on any such outcomes and identified whether any information on chronic lung disease appeared in the primary reports of the randomized controlled trials but not in the systematic reviews.Whether availability of chronic lung disease outcomes differed by type of population and intervention and whether additional non-extracted data might have been available in trial reports.174 systematic reviews with 1041 trials exclusively concerned preterm infants. Of those, 105 reviews looked for chronic lung disease outcomes, and 79 reported on these outcomes. Of the 1041 included trials, 202 reported on chronic lung disease at 28 days and 200 at 36 weeks postmenstrual; 320 reported on chronic lung disease with any definition. The proportion of systematic reviews that looked for or reported on chronic lung disease and the proportion of trials that reported on chronic lung disease was larger in preterm infants with respiratory distress or support than others (P<0.001) and differed across interventions (P<0.001). Even for trials on children with ventilation interventions, only 56% (48/86) reported on chronic lung disease. In the random sample, 45 of 84 trials (54%) had no outcomes on chronic lung disease in the systematic reviews, and only 9/45 (20%) had such information in the primary trial reports.Most trials included in systematic reviews of interventions on preterm infants are missing information on one of the most common serious outcomes in this population. Use of standardized clinical outcomes that would have to be collected and reported by default in all trials in a given specialty should be considered.

Abstract

To summarise the evidence and evaluate the validity of the associations between type 2 diabetes and the risk of developing or dying from cancer.An umbrella review of the evidence across meta-analyses of observational studies of type 2 diabetes with risk of developing or dying from any cancer.PubMed, Embase, Cochrane database of systematic reviews, and manual screening of references.Meta-analyses or systematic reviews of observational studies in humans that examined the association between type 2 diabetes and risk of developing or dying from cancer.Eligible meta-analyses assessed associations between type 2 diabetes and risk of developing cancer in 20 sites and mortality for seven cancer sites. The summary random effects estimates were significant at P=0.05 in 20 meta-analyses (74%); and all reported increased risks of developing cancer for participants with versus without diabetes. Of the 27 meta-analyses, eventually only seven (26%) compiled evidence on more than 1000 cases, had significant summary associations at P ≤ 0.001 for both random and fixed effects calculations, and had neither evidence of small study effects nor evidence for excess significance. Of those, only six (22%) did not have substantial heterogeneity (I(2)>75%), pertaining to associations between type 2 diabetes and risk of developing breast, cholangiocarcinoma (both intrahepatic and extrahepatic), colorectal, endometrial, and gallbladder cancer. The 95% prediction intervals excluded the null value for four of these associations (breast, intrahepatic cholangiocarcinoma, colorectal, and endometrial cancer).Though type 2 diabetes has been extensively studied in relation to risk of developing cancer and cancer mortality and strong claims of significance exist for most of the studied associations, only a minority of these associations have robust supporting evidence without hints of bias.

Abstract

Medical and scientific advances are predicated on new knowledge that is robust and reliable and that serves as a solid foundation on which further advances can be built. In biomedical research, we are in the midst of a revolution with the generation of new data and scientific publications at a previously unprecedented rate. However, unfortunately, there is compelling evidence that the majority of these discoveries will not stand the test of time. To a large extent, this reproducibility crisis in basic and preclinical research may be as a result of failure to adhere to good scientific practice and the desperation to publish or perish. This is a multifaceted, multistakeholder problem. No single party is solely responsible, and no single solution will suffice. Here we review the reproducibility problems in basic and preclinical biomedical research, highlight some of the complexities, and discuss potential solutions that may help improve research quality and reproducibility.

Abstract

To evaluate how often newly developed risk prediction models undergo external validation and how well they perform in such validations.We reviewed derivation studies of newly proposed risk models and their subsequent external validations. Study characteristics, outcome(s), and models' discriminatory performance [area under the curve, (AUC)] in derivation and validation studies were extracted. We estimated the probability of having a validation, change in discriminatory performance with more stringent external validation by overlapping or different authors compared to the derivation estimates.We evaluated 127 new prediction models. Of those, for 32 models (25%), at least an external validation study was identified; in 22 models (17%), the validation had been done by entirely different authors. The probability of having an external validation by different authors within 5 years was 16%. AUC estimates significantly decreased during external validation vs. the derivation study [median AUC change: -0.05 (P < 0.001) overall; -0.04 (P = 0.009) for validation by overlapping authors; -0.05 (P < 0.001) for validation by different authors]. On external validation, AUC decreased by at least 0.03 in 19 models and never increased by at least 0.03 (P < 0.001).External independent validation of predictive models in different studies is uncommon. Predictive performance may worsen substantially on external validation.

How Good Is "Evidence" from Clinical Studies of Drug Effects and Why Might Such Evidence Fail in the Prediction of the Clinical Utility of Drugs?ANNUAL REVIEW OF PHARMACOLOGY AND TOXICOLOGY, VOL 55Naci, H., Ioannidis, J. P.2015; 55: 169-189

Abstract

Promising evidence from clinical studies of drug effects does not always translate to improvements in patient outcomes. In this review, we discuss why early evidence is often ill suited to the task of predicting the clinical utility of drugs. The current gap between initially described drug effects and their subsequent clinical utility results from deficits in the design, conduct, analysis, reporting, and synthesis of clinical studies-often creating conditions that generate favorable, but ultimately incorrect, conclusions regarding drug effects. There are potential solutions that could improve the relevance of clinical evidence in predicting the real-world effectiveness of drugs. What is needed is a new emphasis on clinical utility, with nonconflicted entities playing a greater role in the generation, synthesis, and interpretation of clinical evidence. Clinical studies should adopt strong design features, reflect clinical practice, and evaluate outcomes and comparisons that are meaningful to patients. Transformative changes to the research agenda may generate more meaningful and accurate evidence on drug effects to guide clinical decision making.

Abstract

Osteoarthritis (OA) is the most common form of arthritis with a clear genetic component. To identify novel loci associated with hip OA we performed a meta-analysis of genome-wide association studies (GWAS) on European subjects.We performed a two-stage meta-analysis on more than 78 000 participants. In stage 1, we synthesised data from eight GWAS whereas data from 10 centres were used for 'in silico' or 'de novo' replication. Besides the main analysis, a stratified by sex analysis was performed to detect possible sex-specific signals. Meta-analysis was performed using inverse-variance fixed effects models. A random effects approach was also used.We accumulated 11 277 cases of radiographic and symptomatic hip OA. We prioritised eight single nucleotide polymorphism (SNPs) for follow-up in the discovery stage (4349 OA cases); five from the combined analysis, two male specific and one female specific. One locus, at 20q13, represented by rs6094710 (minor allele frequency (MAF) 4%) near the NCOA3 (nuclear receptor coactivator 3) gene, reached genome-wide significance level with p=7.9×10(-9) and OR=1.28 (95% CI 1.18 to 1.39) in the combined analysis of discovery (p=5.6×10(-8)) and follow-up studies (p=7.3×10(-4)). We showed that this gene is expressed in articular cartilage and its expression was significantly reduced in OA-affected cartilage. Moreover, two loci remained suggestive associated; rs5009270 at 7q31 (MAF 30%, p=9.9×10(-7), OR=1.10) and rs3757837 at 7p13 (MAF 6%, p=2.2×10(-6), OR=1.27 in male specific analysis).Novel genetic loci for hip OA were found in this meta-analysis of GWAS.

Abstract

The publicly available online database MelGene provides a comprehensive, regularly updated, collection of data from genetic association studies in cutaneous melanoma (CM), including random-effects meta-analysis results of all eligible polymorphisms. The updated database version includes data from 192 publications with information on 1114 significantly associated polymorphisms across 280 genes, along with new front-end and back-end capabilities. Various types of relationships between data are calculated and visualized as networks. We constructed 13 different networks containing the polymorphisms and the genes included in MelGene. We explored the derived network representations under the following questions: (i) are there nodes that deserve consideration regarding their network connectivity characteristics? (ii) What is the relation of either the genome-wide or nominally significant CM polymorphisms/genes with the ones highlighted by the network representation? We show that our network approach using the MelGene data reveals connections between statistically significant genes/ polymorphisms and other genes/polymorphisms acting as 'hubs' in the reconstructed networks. To the best of our knowledge, this is the first database containing data from a comprehensive field synopsis and systematic meta-analyses of genetic polymorphisms in CM that provides user-friendly tools for in-depth molecular network visualization and exploration. The proposed network connections highlight potentially new loci requiring further investigation of their relation to melanoma risk. Database URL: http://www.melgene.org.

Abstract

Meta-analysis aims to synthesize results from different studies. Although, in a meta-analysis the presence of large between-study heterogeneity is routinely evaluated, in some instances is also important to probe whether there is extreme between-study homogeneity (i.e. extreme low between-study heterogeneity). HELOW (HEterogeneity LOW) is a program for testing extreme homogeneity in a meta-analysis of risk ratios when binary outcome and Mantel-Haenszel fixed effects summary risk ratio estimate are employed. The significance of extreme homogeneity is assessed using a Monte Carlo test. Extreme homogeneity may yield insights for the statistical and clinical interpretation of the data.

Placing epidemiological results in the context of multiplicity and typical correlations of exposuresJOURNAL OF EPIDEMIOLOGY AND COMMUNITY HEALTHPatel, C. J., Ioannidis, J. P.2014; 68 (11): 1096-1100

Abstract

Epidemiological studies evaluate multiple exposures, but the extent of multiplicity often remains non-transparent when results are reported. There is extensive debate in the literature on whether multiplicity should be adjusted for in the design, analysis, and reporting of most epidemiological studies, and, if so, how this should be done. The challenges become more acute in an era where the number of exposures that can be studied (the exposome) can be very large. Here, we argue that it can be very insightful to visualize and describe the extent of multiplicity by reporting the number of effective exposures for each category of exposures being assessed, and to describe the distribution of correlation between exposures and/or between exposures and outcomes in epidemiological datasets. The results of new proposed associations can be placed in the context of this background information. An association can be assigned to a percentile of magnitude of effect based on the distribution of effects seen in the field. We offer an example of how such information can be routinely presented in an epidemiological study/dataset using data on 530 exposure and demographic variables classified in 32 categories in the National Health and Nutrition Examination Survey (NHANES). Effects that survive multiplicity considerations and that are large may be prioritized for further scrutiny.

Abstract

Reanalyses of randomized clinical trial (RCT) data may help the scientific community assess the validity of reported trial results.To identify published reanalyses of RCT data, to characterize methodological and other differences between the original trial and reanalysis, to evaluate the independence of authors performing the reanalyses, and to assess whether the reanalysis changed interpretations from the original article about the types or numbers of patients who should be treated.We completed an electronic search of MEDLINE from inception to March 9, 2014, to identify all published studies that completed a reanalysis of individual patient data from previously published RCTs addressing the same hypothesis as the original RCT. Four data extractors independently screened articles and extracted data.Changes in direction and magnitude of treatment effect, statistical significance, and interpretation about the types or numbers of patients who should be treated.We identified 37 eligible reanalyses in 36 published articles, 5 of which were performed by entirely independent authors (2 based on publicly available data and 2 on data that were provided on request; data availability was unclear for 1). Reanalyses differed most commonly in statistical or analytical approaches (n = 18) and in definitions or measurements of the outcome of interest (n = 12). Four reanalyses changed the direction and 2 changed the magnitude of treatment effect, whereas 4 led to changes in statistical significance of findings. Thirteen reanalyses (35%) led to interpretations different from that of the original article, 3 (8%) showing that different patients should be treated; 1 (3%), that fewer patients should be treated; and 9 (24%), that more patients should be treated.A small number of reanalyses of RCTs have been published to date. Only a few were conducted by entirely independent authors. Thirty-five percent of published reanalyses led to changes in findings that implied conclusions different from those of the original article about the types and number of patients who should be treated.

Abstract

We conducted a meta-analysis of Parkinson's disease genome-wide association studies using a common set of 7,893,274 variants across 13,708 cases and 95,282 controls. Twenty-six loci were identified as having genome-wide significant association; these and 6 additional previously reported loci were then tested in an independent set of 5,353 cases and 5,551 controls. Of the 32 tested SNPs, 24 replicated, including 6 newly identified loci. Conditional analyses within loci showed that four loci, including GBA, GAK-DGKQ, SNCA and the HLA region, contain a secondary independent risk variant. In total, we identified and replicated 28 independent risk variants for Parkinson's disease across 24 loci. Although the effect of each individual locus was small, risk profile analysis showed substantial cumulative risk in a comparison of the highest and lowest quintiles of genetic risk (odds ratio (OR) = 3.31, 95% confidence interval (CI) = 2.55-4.30; P = 2 × 10(-16)). We also show six risk loci associated with proximal gene expression or DNA methylation.

Abstract

Humans interact with food daily. Such repeated exposure creates a widespread, superficial familiarity with nutrition. Personal familiarity with nutrition from individual and cultural perspectives may give rise to beliefs about food not grounded in scientific evidence. In this summary of the session entitled “Unscientific Beliefs about Scientific Topics in Nutrition,” we discuss accumulated work illustrating and quantifying potentially misleading practices in the conduct and, more so, reporting of nutrition science along with proposed approaches to amelioration. We begin by defining “unscientific beliefs” and from where such beliefs may come, followed by discussing how large bodies of nutritional epidemiologic observations not only create highly improbable patterns of association but implausible magnitudes of implied effect. Poor reporting practices, biases, and methodologic issues that have distorted scientific understandings of nutrition are presented, followed by potential influences of conflicts of interest that extend beyond financial considerations. We conclude with recommendations for improving the conduct, reporting, and communication of nutrition-related research to ground discussions in evidence rather than solely on beliefs.

Abstract

Research for the use of biomarkers in osteoarthritis (OA) is promising, however, adequate discrimination between patients and controls may be hampered due to innate differences. We set out to identify loci influencing levels of serum cartilage oligomeric protein (sCOMP) and urinary C-telopeptide of type II collagen (uCTX-II).Meta-analysis of genome-wide association studies was applied to standardised residuals of sCOMP (N=3316) and uCTX-II (N=4654) levels available in 6 and 7 studies, respectively, from TreatOA. Effects were estimated using a fixed-effects model. Six promising signals were followed up by de novo genotyping in the Cohort Hip and Cohort Knee study (N = 964). Subsequently, their role in OA susceptibility was investigated in large-scale genome-wide association studies meta-analyses for OA. Differential expression of annotated genes was assessed in cartilage.Genome-wide significant association with sCOMP levels was found for a SNP within MRC1 (rs691461, p = 1.7 × 10(-12)) and a SNP within CSMD1 associated with variation in uCTX-II levels with borderline genome-wide significance (rs1983474, p = 8.5 × 10(-8)). Indication for association with sCOMP levels was also found for a locus close to the COMP gene itself (rs10038, p = 7.1 × 10(-6)). The latter SNP was subsequently found to be associated with hip OA whereas COMP expression appeared responsive to the OA pathophysiology in cartilage.We have identified genetic loci affecting either uCTX-II or sCOMP levels. The genome wide significant association of MRC1 with sCOMP levels was found likely to act independent of OA subtypes. Increased sensitivity of biomarkers with OA may be accomplished by taking genetic variation into account.

Abstract

To assess the early changes of soluble IFN-γ, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12, TNF-α, TNF-β, IL-17A, IL-22, soluble (s) P-Selectin, sE-Selectin and sICAM-1 in post-ERCP pancreatitis (PEP).Single center, prospective study of 318 ERCP procedures. Serum samples were acquired from all patients prior to ERCP, 6hours and 24hours after the procedure. For every PEP case, another patient was chosen as a control, matched for gender, age and time period in which ERCP took place.Totally, 28 cases and 28 controls were studied. Except for significantly higher IL-1b levels in cases at baseline, no significant differences were observed between cases and controls after Bonferroni corrections. An increase in IL-6 was noted between baseline and 6h in cases alone (p=0.016). There was a significant fall in sP-selectin levels at 6 and 24hours compared to baseline in all patients (corrected p=0.008 and 0.016 for cases and 0.016 and 0.048 for controls respectively). An increase of sE-selectin in cases was observed between 6 and 24hours post-ERCP (corrected p=0.03).Soluble forms of cytokines and adhesion molecules studied seem not to play a major role in PEP.

Abstract

Meta-analyses of epidemiologic studies have suggested that metformin may reduce cancer incidence, but randomized controlled trials did not support this hypothesis.RESEARCH DESIGN AND METHODS: A retrospective cohort study, Clinical Practice Research Datalink, was designed to investigate the association between use of metformin compared with other antidiabetes medications and cancer risk by emulating an intention-to-treat analysis as in a trial. A total of 95,820 participants with type 2 diabetes who started taking metformin and other oral antidiabetes medications within 12 months of their diagnosis (initiators) were followed up for first-incident cancer diagnosis without regard to any subsequent changes in pharmacotherapy. Cox proportional hazards models were used to estimate multivariable-adjusted hazard ratios (HR) and 95% CI.RESULTS: A total of 51,484 individuals (54%) were metformin initiators and 18,264 (19%) were sulfonylurea initiators, and 3,805 first-incident cancers were diagnosed during a median follow-up time of 5.1 years. Compared with initiators of sulfonylurea, initiators of metformin had a similar incidence of total cancer (HR 0.96; 95% CI 0.89-1.04) and colorectal (HR 0.92; 95% CI 0.76-1.13), prostate (HR 1.02; 95% CI 0.83-1.25), lung (HR 0.85; 95% CI 0.68-1.07), or postmenopausal breast (HR 1.03; 95% CI 0.82-1.31) cancer, or any other cancer.CONCLUSIONS: In this large study, individuals with diabetes who used metformin had a similar risk of developing cancer compared with those who used sulfonylureas.

Abstract

microRNAs (miRNAs) are fundamental to cellular biology. Although only approximately 22 bases long, miRNAs regulate complex processes in health and disease, including human cancer. Because miRNAs are highly stable in circulation when compared with several other classes of nucleic acids, they have generated intense interest as clinical biomarkers in diverse epidemiologic studies. As with other molecular biomarker fields, however, miRNA research has become beleaguered by pitfalls related to terminology and classification; procedural, assay, and study cohort heterogeneity; and methodological inconsistencies. Together, these issues have led to both false-positive and potentially false-negative miRNA associations. In this review, we summarize the biological rationale for studying miRNAs in human disease with a specific focus on circulating miRNAs, which highlight some of the most challenging topics in the field to date. Examples from lung cancer are used to illustrate the potential utility and some of the pitfalls in contemporary miRNA research. Although the field is in its infancy, several important lessons have been learned relating to cohort development, sample preparation, and statistical analysis that should be considered for future studies. The goal of this primer is to equip epidemiologists and clinical researchers with sound principles of study design and analysis when using miRNAs.

Abstract

The inaugural round of merit review for the Patient-Centered Outcomes Research Institute (PCORI) in November 2012 included patients and other stakeholders, as well as scientists. This article examines relationships among scores of the 3 reviewer types, changes in scoring after in-person discussion, and the effect of inclusion of patient and stakeholder reviewers on the review process. In the first phase, 363 scientists scored 480 applications. In the second phase, 59 scientists, 21 patients, and 31 stakeholders provided a "prediscussion" score and a final "postdiscussion" score after an in-person meeting for applications. Bland-Altman plots were used to characterize levels of agreement among and within reviewer types before and after discussion. Before discussion, there was little agreement among average scores given by the 4 lead scientific reviewers and patient and stakeholder reviewers. After discussion, the 4 primary reviewers showed mild convergence in their scores, and the 21-member panel came to a much stronger agreement. Of the 25 awards with the best (and lowest) scores after phase 2, only 13 had ranked in the top 25 after the phase 1 review by scientists. Five percent of the 480 proposals submitted were funded. The authors conclude that patient and stakeholder reviewers brought different perspectives to the review process but that in-person discussion led to closer agreement among reviewer types. It is not yet known whether these conclusions are generalizable to future rounds of peer review. Future work would benefit from additional data collection for evaluation purposes and from long-term evaluation of the effect on the funded research.

Abstract

Metabolomics is the field of "-omics" research concerned with the comprehensive characterization of the small low-molecular-weight metabolites in biological samples. In epidemiology, it represents an emerging technology and an unprecedented opportunity to measure environmental and other exposures with improved precision and far less measurement error than with standard epidemiologic methods. Advances in the application of metabolomics in large-scale epidemiologic research are now being realized through a combination of improved sample preparation and handling, automated laboratory and processing methods, and reduction in costs. The number of epidemiologic studies that use metabolic profiling is still limited, but it is fast gaining popularity in this area. In the present article, we present a roadmap for metabolomic analyses in epidemiologic studies and discuss the various challenges these data pose to large-scale studies. We discuss the steps of data preprocessing, univariate and multivariate data analysis, correction for multiplicity of comparisons with correlated data, and finally the steps of cross-validation and external validation. As data from metabolomic studies accumulate in epidemiology, there is a need for large-scale replication and synthesis of findings, increased availability of raw data, and a focus on good study design, all of which will highlight the potential clinical impact of metabolomics in this field.

Abstract

The ability of a scientist to maintain a continuous stream of publication may be important, because research requires continuity of effort. However, there is no data on what proportion of scientists manages to publish each and every year over long periods of time.Using the entire Scopus database, we estimated that there are 15,153,100 publishing scientists (distinct author identifiers) in the period 1996-2011. However, only 150,608 (<1%) of them have published something in each and every year in this 16-year period (uninterrupted, continuous presence [UCP] in the literature). This small core of scientists with UCP are far more cited than others, and they account for 41.7% of all papers in the same period and 87.1% of all papers with >1000 citations in the same period. Skipping even a single year substantially affected the average citation impact. We also studied the birth and death dynamics of membership in this influential UCP core, by imputing and estimating UCP-births and UCP-deaths. We estimated that 16,877 scientists would qualify for UCP-birth in 1997 (no publication in 1996, UCP in 1997-2012) and 9,673 scientists had their UCP-death in 2010. The relative representation of authors with UCP was enriched in Medical Research, in the academic sector and in Europe/North America, while the relative representation of authors without UCP was enriched in the Social Sciences and Humanities, in industry, and in other continents.The proportion of the scientific workforce that maintains a continuous uninterrupted stream of publications each and every year over many years is very limited, but it accounts for the lion's share of researchers with high citation impact. This finding may have implications for the structure, stability and vulnerability of the scientific workforce.

Abstract

Clinical decisions should be based on the totality of the best evidence and not the results of individual studies. When clinicians apply the results of a systematic review or meta-analysis to patient care, they should start by evaluating the credibility of the methods of the systematic review, ie, the extent to which these methods have likely protected against misleading results. Credibility depends on whether the review addressed a sensible clinical question; included an exhaustive literature search; demonstrated reproducibility of the selection and assessment of studies; and presented results in a useful manner. For reviews that are sufficiently credible, clinicians must decide on the degree of confidence in the estimates that the evidence warrants (quality of evidence). Confidence depends on the risk of bias in the body of evidence; the precision and consistency of the results; whether the results directly apply to the patient of interest; and the likelihood of reporting bias. Shared decision making requires understanding of the estimates of magnitude of beneficial and harmful effects, and confidence in those estimates.

Abstract

Multiple interventions have been tested in acute respiratory distress syndrome (ARDS). We examined the entire agenda of published randomized controlled trials (RCTs) in ARDS that reported on mortality and of respective meta-analyses.We searched PubMed, the Cochrane Library, and Web of Knowledge until July 2013. We included RCTs in ARDS published in English. We excluded trials of newborns and children; and those on short-term interventions, ARDS prevention, or post-traumatic lung injury. We also reviewed all meta-analyses of RCTs in this field that addressed mortality. Treatment modalities were grouped in five categories: mechanical ventilation strategies and respiratory care, enteral or parenteral therapies, inhaled/intratracheal medications, nutritional support, and hemodynamic monitoring.We identified 159 published RCTs of which 93 had overall mortality reported (n = 20,671 patients)-44 trials (14,426 patients) reported mortality as a primary outcome. A statistically significant survival benefit was observed in eight trials (seven interventions) and two trials reported an adverse effect on survival. Among RCTs with more than 50 deaths in at least one treatment arm (n = 21), two showed a statistically significant mortality benefit of the intervention (lower tidal volumes and prone positioning), one showed a statistically significant mortality benefit only in adjusted analyses (cisatracurium), and one (high-frequency oscillatory ventilation) showed a significant detrimental effect. Across 29 meta-analyses, the most consistent evidence was seen for low tidal volumes and prone positioning in severe ARDS.There is limited supportive evidence that specific interventions can decrease mortality in ARDS. While low tidal volumes and prone positioning in severe ARDS seem effective, most sporadic findings of interventions suggesting reduced mortality are not corroborated consistently in large-scale evidence including meta-analyses.

Abstract

To evaluate the effects of diagnostic testing on patient outcomes in a large sample of diagnostic randomized controlled trials (D-RCTs) and to examine whether the effects for patient outcomes correlate with the effects on management and with diagnostic accuracy.We considered D-RCTs that evaluated diagnostic interventions for any condition and reported effectiveness data on one or more patient outcomes. We calculated odds ratios for patient outcomes and outcomes pertaining to the use of further diagnostic and therapeutic interventions and the diagnostic odds ratio (DOR) for the accuracy of experimental tests.One hundred forty trials (153 comparisons) were eligible. Patient outcomes were significantly improved in 28 comparisons (18%). There was no concordance in significance and direction of effects between the patient outcome and outcomes for use of further diagnostic or therapeutic interventions (weighted κ 0.02 and 0.09, respectively). The effect size for the patient outcome did not correlate with the effect sizes for use of further diagnostic (r = 0.05; P = 0.78) or therapeutic interventions (r = 0.18; P = 0.08) or the experimental intervention DOR in the same trial (r = -0.24; P = 0.51).Few tests have well-documented benefits on patient outcomes. Diagnostic performance or the effects on management decisions are not necessarily indicative of patient benefits.

Abstract

To evaluate the extent of non-publication or delayed publication of registered randomized trials on vaccines, and to investigate potential determinants of delay to publication.Survey.Trials registry websites, Scopus, PubMed, Google.Randomized controlled trials evaluating the safety or the efficacy or immunogenicity of human papillomavirus (HPV), pandemic A/H1N1 2009 influenza, and meningococcal, pneumococcal, and rotavirus vaccines that were registered in ClinicalTrials.gov, Current Controlled Trials, WHO International Clinical Trials Registry Platform, Clinical Study Register, or Indian, Australian-New Zealand, and Chinese trial registries in 2006-12. Electronic databases were searched up to February 2014 to identify published manuscripts containing trial results. These were reviewed and classified as positive, mixed, or negative. We also reviewed the results available in ClinicalTrials.gov.Publication status of trial results and time from completion to publication in peer reviewed journals.Cox proportional hazards regression was used to evaluate potential predictors of publication delay.We analysed 384 trials (85% sponsored by industry). Of 355 trials (404 758 participants) that were completed, 176 (n=151 379) had been published in peer reviewed journals. Another 42 trials (total sample 62 765) remained unpublished but reported results in ClinicalTrials.gov. The proportion of trials published 12, 24, 36, and 48 months after completion was 12%, 29%, 53%, and 73%, respectively. Including results posted in ClinicalTrials.gov, 48 months after study completion results were available for 82% of the trials and 90% of the participants. Delay to publication between non-industry and industry sponsored trials did not differ, but non-industry sponsored trials were 4.42-fold (P=0.008) more likely to report negative or mixed findings. Negative results were reported by only 2% of the published trials.Most vaccine trials are published eventually or the results posted in ClinicalTrials.gov, but delays to publication of several years are common. Actions should focus on the timely dissemination of data from vaccine trials to the public.

Abstract

Recent systematic reviews and empirical evaluations of the cognitive sciences literature suggest that publication and other reporting biases are prevalent across diverse domains of cognitive science. In this review, we summarize the various forms of publication and reporting biases and other questionable research practices, and overview the available methods for probing into their existence. We discuss the available empirical evidence for the presence of such biases across the neuroimaging, animal, other preclinical, psychological, clinical trials, and genetics literature in the cognitive sciences. We also highlight emerging solutions (from study design to data analyses and reporting) to prevent bias and improve the fidelity in the field of cognitive science research.

Abstract

To analyse evidence from randomized controlled trials (RCTs) on the prevention and control of neglected tropical diseases (NTDs) and to identify areas where evidence is lacking.The Cochrane Central Register of Controlled Trials and PubMed were searched for RCTs and the Cochrane Database of Systematic Reviews and PubMed were searched for meta-analyses and systematic reviews, both from inception to 31 December 2012.Overall, 258 RCTs were found on American trypanosomiasis, Buruli ulcer, dengue, geohelminth infection, leishmaniasis, leprosy, lymphatic filariasis, onchocerciasis, rabies, schistosomiasis or trachoma. No RCTs were found on cysticercosis, dracunculiasis, echinococcosis, foodborne trematodes, or human African trypanosomiasis. The most studied diseases were geohelminth infection (51 RCTs) and leishmaniasis (46 RCTs). Vaccines, chemoprophylaxis and interventions targeting insect vectors were evaluated in 113, 99 and 39 RCTs, respectively. Few addressed how best to deliver preventive chemotherapy, such as the choice of dosing interval (10) or target population (4), the population coverage needed to reduce transmission (2) or the method of drug distribution (1). Thirty-one publications containing 32 systematic reviews (16 with and 16 without meta-analyses) were found on American trypanosomiasis, dengue, geohelminths, leishmaniasis, leprosy, lymphatic filariasis, onchocerciasis, schistosomiasis or trachoma. Together, they included only 79 of the 258 published RCTs (30.6%). Of 36 interventions assessed, 8 were judged effective in more than one review.Few RCTs on the prevention or control of the principal NTDs were found. Trials on how best to deliver preventive chemotherapy were particularly rare.

Abstract

To systematically evaluate the use of Framingham Risk Score (FRS) in the medical literature and specifically examine the use of FRS in different populations and settings and for different outcomes than the ones originally developed for.We identified all the citations to the article by Wilson et al. (1998), in which FRS was originally described through ISI Web of Science until April 2011. We selected studies that stated in their abstract that they calculated or used the FRS for any reason and extracted information on publication date, population studied, outcome, or disease risk factor with which FRS was associated and study design.We identified 375 eligible articles corresponding to 471 analyses using the FRS in cohort (n = 141), case-control (n = 16), or cross-sectional (n = 314) settings. Only a minority of the cohort studies had as a primary aim to externally validate the FRS (n = 45). The studied population was different (from general or healthy) in 35 (25%) and 133 (42%) of the cohort and cross-sectional analyses, respectively. All case-control studies examined healthy controls. The studied outcome was different (from coronary heart disease) in 79 (56%) of the cohort analyses and 10 (63%) of the case-control studies. Overall, only 46 (33%) of the 141 cohort analyses examined the same outcome and population as FRS was originally developed for.A large number of studies use FRS in populations and for outcomes other than the ones it has been developed for and therefore for which its performance is unknown and nonvalidated.

Abstract

Our main objective was to raise awareness of the areas that need improvements in the reporting of genetic risk prediction articles for future publications, based on the Genetic RIsk Prediction Studies (GRIPS) statement.We evaluated studies that developed or validated a prediction model based on multiple DNA variants, using empirical data, and were published in 2010. A data extraction form based on the 25 items of the GRIPS statement was created and piloted.Forty-two studies met our inclusion criteria. Overall, more than half of the evaluated items (34 of 62) were reported in at least 85% of included articles. Seventy-seven percentage of the articles were identified as genetic risk prediction studies through title assessment, but only 31% used the keywords recommended by GRIPS in the title or abstract. Seventy-four percentage mentioned which allele was the risk variant. Overall, only 10% of the articles reported all essential items needed to perform external validation of the risk model.Completeness of reporting in genetic risk prediction studies is adequate for general elements of study design but is suboptimal for several aspects that characterize genetic risk prediction studies such as description of the model construction. Improvements in the transparency of reporting of these aspects would facilitate the identification, replication, and application of genetic risk prediction models.

Abstract

The study of genetic influences on drug response and efficacy ('pharmacogenetics') has existed for over 50 years. Yet, we still lack a complete picture of how genetic variation, both common and rare, affects each individual's responses to medications. Exome sequencing is a promising alternative method for pharmacogenetic discovery as it provides information on both common and rare variation in large numbers of individuals. Using exome data from 2203 AA and 4300 Caucasian individuals through the NHLBI Exome Sequencing Project, we conducted a survey of coding variation within 12 Cytochrome P450 (CYP) genes that are collectively responsible for catalyzing nearly 75% of all known Phase I drug oxidation reactions. In addition to identifying many polymorphisms with known pharmacogenetic effects, we discovered over 730 novel nonsynonymous alleles across the 12 CYP genes of interest. These alleles include many with diverse functional effects such as premature stop codons, aberrant splicesites and mutations at conserved active site residues. Our analysis considering both novel, predicted functional alleles as well as known, actionable CYP alleles reveals that rare, deleterious variation contributes markedly to the overall burden of pharmacogenetic alleles within the populations considered, and that the contribution of rare variation to this burden is over three times greater in AA individuals as compared with Caucasians. While most of these impactful alleles are individually rare, 7.6-11.7% of individuals interrogated in the study carry at least one newly described potentially deleterious alleles in a major drug-metabolizing CYP.

Abstract

To evaluate the breadth, validity, and presence of biases of the associations of vitamin D with diverse outcomes.Umbrella review of the evidence across systematic reviews and meta-analyses of observational studies of plasma 25-hydroxyvitamin D or 1,25-dihydroxyvitamin D concentrations and randomised controlled trials of vitamin D supplementation.Medline, Embase, and screening of citations and references.Three types of studies were eligible for the umbrella review: systematic reviews and meta-analyses that examined observational associations between circulating vitamin D concentrations and any clinical outcome; and meta-analyses of randomised controlled trials assessing supplementation with vitamin D or active compounds (both established and newer compounds of vitamin D).107 systematic literature reviews and 74 meta-analyses of observational studies of plasma vitamin D concentrations and 87 meta-analyses of randomised controlled trials of vitamin D supplementation were identified. The relation between vitamin D and 137 outcomes has been explored, covering a wide range of skeletal, malignant, cardiovascular, autoimmune, infectious, metabolic, and other diseases. Ten outcomes were examined by both meta-analyses of observational studies and meta-analyses of randomised controlled trials, but the direction of the effect and level of statistical significance was concordant only for birth weight (maternal vitamin D status or supplementation). On the basis of the available evidence, an association between vitamin D concentrations and birth weight, dental caries in children, maternal vitamin D concentrations at term, and parathyroid hormone concentrations in patients with chronic kidney disease requiring dialysis is probable, but further studies and better designed trials are needed to draw firmer conclusions. In contrast to previous reports, evidence does not support the argument that vitamin D only supplementation increases bone mineral density or reduces the risk of fractures or falls in older people.Despite a few hundred systematic reviews and meta-analyses, highly convincing evidence of a clear role of vitamin D does not exist for any outcome, but associations with a selection of outcomes are probable.

Abstract

To assess candidate genes for association with osteoarthritis (OA) and identify promising genetic factors and, secondarily, to assess the candidate gene approach in OA.A total of 199 candidate genes for association with OA were identified using Human Genome Epidemiology (HuGE) Navigator. All of their single-nucleotide polymorphisms (SNPs) with an allele frequency of >5% were assessed by fixed-effects meta-analysis of 9 genome-wide association studies (GWAS) that included 5,636 patients with knee OA and 16,972 control subjects and 4,349 patients with hip OA and 17,836 control subjects of European ancestry. An additional 5,921 individuals were genotyped for significantly associated SNPs in the meta-analysis. After correction for the number of independent tests, P values less than 1.58 × 10(-5) were considered significant.SNPs at only 2 of the 199 candidate genes (COL11A1 and VEGF) were associated with OA in the meta-analysis. Two SNPs in COL11A1 showed association with hip OA in the combined analysis: rs4907986 (P = 1.29 × 10(-5) , odds ratio [OR] 1.12, 95% confidence interval [95% CI] 1.06-1.17) and rs1241164 (P = 1.47 × 10(-5) , OR 0.82, 95% CI 0.74-0.89). The sex-stratified analysis also showed association of COL11A1 SNP rs4908291 in women (P = 1.29 × 10(-5) , OR 0.87, 95% CI 0.82-0.92); this SNP showed linkage disequilibrium with rs4907986. A single SNP of VEGF, rs833058, showed association with hip OA in men (P = 1.35 × 10(-5) , OR 0.85, 95% CI 0.79-0.91). After additional samples were genotyped, association at one of the COL11A1 signals was reinforced, whereas association at VEGF was slightly weakened.Two candidate genes, COL11A1 and VEGF, were significantly associated with OA in this focused meta-analysis. The remaining candidate genes were not associated.

Abstract

Basal cell carcinoma (BCC) is the most common cancer with 2 million treatments per year with little evidence-based guidelines for treatment. There are three classes of interventions (surgical, destructive, and topical) for BCC, and this study aimed to determine whether there are preferences or avoidances in comparisons of different types of treatments for BCC in randomized controlled trials (RCTs).PubMed, Cochrane Central Registry of Clinical Trials, and ClinicalTrials.Gov were used to identify eligible published and registered ongoing RCTs.Fifty-five trials (42 published and 13 registered trials) were identified. Only one unpublished registered trial compared a topical vs. a surgical intervention, and only one trial compared a topical vs. a destructive intervention. Conversely, 44 of the 55 trials compared interventions within the same treatment class and 9 of 55 trials compared surgical vs. destructive interventions. In most trials, selection of same-class comparators was not necessitated by the type of BCC lesions (nonaggressive superficial or nodular vs. aggressive, infiltrative, morpheic BCCs, P = 0.155) or their location (face vs. nonfacial, P = 0.137).This is the first time that an evaluation of network geometry is applied to address issues of comparisons between different families of interventions that belong to different specialties and practices (medical vs. surgical). Previous evaluations of homophily have addressed different families of interventions, in which all interventions are medical (drugs) and performed in the same health-care settings. The noncommunicating bodies of evidence between medical and surgical interventions that we document highlight a problem of unnecessary sequestration of the evidence and the corresponding health-care practices.

Abstract

Whole-genome sequencing (WGS) is increasingly applied in clinical medicine and is expected to uncover clinically significant findings regardless of sequencing indication.To examine coverage and concordance of clinically relevant genetic variation provided by WGS technologies; to quantitate inherited disease risk and pharmacogenomic findings in WGS data and resources required for their discovery and interpretation; and to evaluate clinical action prompted by WGS findings.An exploratory study of 12 adult participants recruited at Stanford University Medical Center who underwent WGS between November 2011 and March 2012. A multidisciplinary team reviewed all potentially reportable genetic findings. Five physicians proposed initial clinical follow-up based on the genetic findings.Genome coverage and sequencing platform concordance in different categories of genetic disease risk, person-hours spent curating candidate disease-risk variants, interpretation agreement between trained curators and disease genetics databases, burden of inherited disease risk and pharmacogenomic findings, and burden and interrater agreement of proposed clinical follow-up.Depending on sequencing platform, 10% to 19% of inherited disease genes were not covered to accepted standards for single nucleotide variant discovery. Genotype concordance was high for previously described single nucleotide genetic variants (99%-100%) but low for small insertion/deletion variants (53%-59%). Curation of 90 to 127 genetic variants in each participant required a median of 54 minutes (range, 5-223 minutes) per genetic variant, resulted in moderate classification agreement between professionals (Gross κ, 0.52; 95% CI, 0.40-0.64), and reclassified 69% of genetic variants cataloged as disease causing in mutation databases to variants of uncertain or lesser significance. Two to 6 personal disease-risk findings were discovered in each participant, including 1 frameshift deletion in the BRCA1 gene implicated in hereditary breast and ovarian cancer. Physician review of sequencing findings prompted consideration of a median of 1 to 3 initial diagnostic tests and referrals per participant, with fair interrater agreement about the suitability of WGS findings for clinical follow-up (Fleiss κ, 0.24; P

Abstract

Compare the risk of harm from pharmacologic interventions in pediatric versus adult randomized controlled trials (RCTs).We used systematic reviews from the Cochrane Database of Systematic Reviews. We considered separately 7 categories of harms/harm-related end points: severe harms, withdrawals due to harms, any harm, organ system-level harms, specific harms, withdrawals for any reason, and mortality. Systematic reviews with quantitative synthesis from at least 1 adult and 1 pediatric RCT for any of those end points were eligible. We calculated the summary odds ratio (experimental versus control intervention) in adult and pediatric trials/meta-analysis; the relative odds ratio (ROR) in adults versus children per meta-analysis; and the summary ROR (sROR) across all meta-analyses for each end point. ROR <1 means that the experimental intervention fared worse in children than adults.We identified 176 meta-analyses for 52 types of harms/harm-related end points with 669 adult and 184 pediatric RCTs. Of those, 165 had sufficient data for ROR estimation. sRORs showed statistically significant discrepancy between adults and children only for headache (sROR 0.82; 95% confidence interval 0.70-0.96). Nominally significant discrepancies for specific harms were identified in 12 of 165 meta-analyses (RORs <1 in 7, ROR >1 in 5). In 36% of meta-analyses, the ROR estimates suggested twofold or greater differences between children and adults, and the 95% confidence intervals could exclude twofold differences only in 18% of meta-analyses.Available evidence on harms/harm-related end points from pharmacologic interventions has large uncertainty. Extrapolation of evidence from adults to children may be tenuous. Some clinically important discrepancies were identified.

Abstract

The vast majority of health-related observational studies are not prospectively registered and the advantages of registration have not been fully appreciated. Nonetheless, international standards require approval of study protocols by an independent ethics committee before the study can begin. We suggest that there is an ethical and scientific imperative to publicly preregister key information from newly approved protocols, which should be required by funders. Ultimately, more complete information may be publicly available by disclosing protocols, analysis plans, data sets, and raw data.

Reply to Nuijten et al.: Reanalyses actually confirm that US studies overestimate effects in softer researchPROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICAFanelli, D., Ioannidis, J. P.2014; 111 (7): E714-E715

Abstract

The DerSimonian and Laird approach (DL) is widely used for random effects meta-analysis, but this often results in inappropriate type I error rates. The method described by Hartung, Knapp, Sidik and Jonkman (HKSJ) is known to perform better when trials of similar size are combined. However evidence in realistic situations, where one trial might be much larger than the other trials, is lacking. We aimed to evaluate the relative performance of the DL and HKSJ methods when studies of different sizes are combined and to develop a simple method to convert DL results to HKSJ results.We evaluated the performance of the HKSJ versus DL approach in simulated meta-analyses of 2-20 trials with varying sample sizes and between-study heterogeneity, and allowing trials to have various sizes, e.g. 25% of the trials being 10-times larger than the smaller trials. We also compared the number of "positive" (statistically significant at p < 0.05) findings using empirical data of recent meta-analyses with > = 3 studies of interventions from the Cochrane Database of Systematic Reviews.The simulations showed that the HKSJ method consistently resulted in more adequate error rates than the DL method. When the significance level was 5%, the HKSJ error rates at most doubled, whereas for DL they could be over 30%. DL, and, far less so, HKSJ had more inflated error rates when the combined studies had unequal sizes and between-study heterogeneity. The empirical data from 689 meta-analyses showed that 25.1% of the significant findings for the DL method were non-significant with the HKSJ method. DL results can be easily converted into HKSJ results.Our simulations showed that the HKSJ method consistently results in more adequate error rates than the DL method, especially when the number of studies is small, and can easily be applied routinely in meta-analyses. Even with the HKSJ method, extra caution is needed when there are = <5 studies of very unequal sizes.

Abstract

Elevated low-density lipoprotein cholesterol (LDL-C) is a treatable, heritable risk factor for cardiovascular disease. Genome-wide association studies (GWASs) have identified 157 variants associated with lipid levels but are not well suited to assess the impact of rare and low-frequency variants. To determine whether rare or low-frequency coding variants are associated with LDL-C, we exome sequenced 2,005 individuals, including 554 individuals selected for extreme LDL-C (>98(th) or <2(nd) percentile). Follow-up analyses included sequencing of 1,302 additional individuals and genotype-based analysis of 52,221 individuals. We observed significant evidence of association between LDL-C and the burden of rare or low-frequency variants in PNPLA5, encoding a phospholipase-domain-containing protein, and both known and previously unidentified variants in PCSK9, LDLR and APOB, three known lipid-related genes. The effect sizes for the burden of rare variants for each associated gene were substantially higher than those observed for individual SNPs identified from GWASs. We replicated the PNPLA5 signal in an independent large-scale sequencing study of 2,084 individuals. In conclusion, this large whole-exome-sequencing study for LDL-C identified a gene not known to be implicated in LDL-C and provides unique insight into the design and analysis of similar experiments.

Abstract

Osteoporosis is a systemic skeletal disease characterised by reduced bone mineral density and increased susceptibility to fracture; these traits are highly heritable. Both common and rare copy number variants (CNVs) potentially affect the function of genes and may influence disease risk.To identify CNVs associated with osteoporotic bone fracture risk.We performed a genome-wide CNV association study in 5178 individuals from a prospective cohort in the Netherlands, including 809 osteoporotic fracture cases, and performed in silico lookups and de novo genotyping to replicate in several independent studies.A rare (population prevalence 0.14%, 95% CI 0.03% to 0.24%) 210 kb deletion located on chromosome 6p25.1 was associated with the risk of fracture (OR 32.58, 95% CI 3.95 to 1488.89; p=8.69×10(-5)). We performed an in silico meta-analysis in four studies with CNV microarray data and the association with fracture risk was replicated (OR 3.11, 95% CI 1.01 to 8.22; p=0.02). The prevalence of this deletion showed geographic diversity, being absent in additional samples from Australia, Canada, Poland, Iceland, Denmark, and Sweden, but present in the Netherlands (0.34%), Spain (0.33%), USA (0.23%), England (0.15%), Scotland (0.10%), and Ireland (0.06%), with insufficient evidence for association with fracture risk.These results suggest that deletions in the 6p25.1 locus may predispose to higher risk of fracture in a subset of populations of European origin; larger and geographically restricted studies will be needed to confirm this regional association. This is a first step towards the evaluation of the role of rare CNVs in osteoporosis.

Abstract

There have been over 100 randomized clinical trials (RCTs) of diverse regimens of antiretroviral therapy for treatment-naïve human immunodeficiency virus-positive patients. A further 400 systematic reviews and meta-analyses are informed by these trials. There are, however, difficulties in using systematic reviews and meta-analyses of this clinical evidence to inform guidelines and clinical practice. Several issues can make the interpretation of comparative effectiveness challenging. In this article, we review the key challenges in interpreting multiple trials in this population. We specifically examine the network geometry of the clinical trial comparisons, the predominance of non-inferiority trial designs, issues related to potential class effects, heterogeneous documentation of adverse events, and a relative lack of RCTs that reflect specific current clinical guideline recommendations. We conclude with recommendations for future clinical trials and meta-analyses.

Abstract

Vertebral fracture risk is a heritable complex trait. The aim of this study was to identify genetic susceptibility factors for osteoporotic vertebral fractures applying a genome-wide association study (GWAS) approach. The GWAS discovery was based on the Rotterdam Study, a population-based study of elderly Dutch individuals aged > 55 years; and comprising 329 cases and 2666 controls with radiographic scoring (McCloskey–Kanis) and genetic data. Replication of one top-associated SNP was pursued by de-novo genotyping of 15 independent studies across Europe, the United States, and Australia and one Asian study. Radiographic vertebral fracture assessment was performed using McCloskey–Kanis or Genant semi-quantitative definitions. SNPs were analyzed in relation to vertebral fracture using logistic regression models corrected for age and sex. Fixed effects inverse variance and Han–Eskin alternative random effects meta-analyses were applied. Genome-wide significance was set at p < 5 × 10− 8. In the discovery, a SNP (rs11645938) on chromosome 16q24 was associated with the risk for vertebral fractures at p = 4.6 × 10− 8. However, the association was not significant across 5720 cases and 21,791 controls from 14 studies. Fixed-effects meta-analysis summary estimate was 1.06 (95% CI: 0.98–1.14; p = 0.17), displaying high degree of heterogeneity (I2 = 57%; Qhet p = 0.0006). Under Han–Eskin alternative random effects model the summary effect was significant (p = 0.0005). The SNP maps to a region previously found associated with lumbar spine bone mineral density (LS-BMD) in two large meta-analyses from the GEFOS consortium. A false positive association in the GWAS discovery cannot be excluded, yet, the low-powered setting of the discovery and replication settings (appropriate to identify risk effect size > 1.25) may still be consistent with an effect size < 1.10, more of the type expected in complex traits. Larger effort in studies with standardized phenotype definitions is needed to confirm or reject the involvement of this locus on the risk for vertebral fractures.

Abstract

The United States Preventive Services Task Force (USPSTF) recommends screening adults for depression in primary care settings when staff-assisted depression management programs are available. This recommendation, however, is based on evidence from depression management programs conducted with patients already identified as depressed, even though screening is intended to identify depressed patients not already recognized or treated. The objective of this systematic review was to evaluate whether there is evidence from randomized controlled trials (RCTs) that depression screening benefits patients in primary care, using an explicit definition of screening.We re-evaluated RCTs included in the 2009 USPSTF evidence review on depression screening, including only trials that compared depression outcomes between screened and non-screened patients and met the following three criteria: determined patient eligibility and randomized prior to screening; excluded patients already diagnosed with a recent episode of depression or already being treated for depression; and provided the same level of depression treatment services to patients identified as depressed in the screening and non-screening trial arms. We also reviewed studies included in a recent Cochrane systematic review, but not the USPSTF review; conducted a focused search to update the USPSTF review; and reviewed trial registries.Of the nine RCTs included in the USPSTF review, four fulfilled none of three criteria for a test of depression screening, four fulfilled one of three criteria, and one fulfilled two of three criteria. There were two additional RCTs included only in the Cochrane review, and each fulfilled one of three criteria. No eligible RCTs were found via the updated review.The USPSTF recommendation to screen adults for depression in primary care settings when staff-assisted depression management programs are available is not supported by evidence from any RCTs that are directly relevant to the recommendation. The USPSTF should re-evaluate this recommendation.Registration: PROSPERO (#CRD42013004276).

Abstract

Clinicians, when trying to apply trial results to patient care, need to individualize patient care and, potentially, manage patients based on results of subgroup analyses. Apparently compelling subgroup effects often prove spurious, and guidance is needed to differentiate credible from less credible subgroup claims. We therefore provide 5 criteria to use when assessing the validity of subgroup analyses: (1) Can chance explain the apparent subgroup effect; (2) Is the effect consistent across studies; (3) Was the subgroup hypothesis one of a small number of hypotheses developed a priori with direction specified; (4) Is there strong preexisting biological support; and (5) Is the evidence supporting the effect based on within- or between-study comparisons. The first 4 criteria are applicable to individual studies or systematic reviews, the last only to systematic reviews of multiple studies. These criteria will help clinicians deciding whether to use subgroup analyses to guide their patient care.

Abstract

Correctable weaknesses in the design, conduct, and analysis of biomedical and public health research studies can produce misleading results and waste valuable resources. Small effects can be difficult to distinguish from bias introduced by study design and analyses. An absence of detailed written protocols and poor documentation of research is common. Information obtained might not be useful or important, and statistical precision or power is often too low or used in a misleading way. Insufficient consideration might be given to both previous and continuing studies. Arbitrary choice of analyses and an overemphasis on random extremes might affect the reported findings. Several problems relate to the research workforce, including failure to involve experienced statisticians and methodologists, failure to train clinical researchers and laboratory scientists in research methods and design, and the involvement of stakeholders with conflicts of interest. Inadequate emphasis is placed on recording of research decisions and on reproducibility of research. Finally, reward systems incentivise quantity more than quality, and novelty more than reliability. We propose potential solutions for these problems, including improvements in protocols and documentation, consideration of evidence from studies in progress, standardisation of research efforts, optimisation and training of an experienced and non-conflicted scientific workforce, and reconsideration of scientific reward systems.

Abstract

The increase in annual global investment in biomedical research--reaching US$240 billion in 2010--has resulted in important health dividends for patients and the public. However, much research does not lead to worthwhile achievements, partly because some studies are done to improve understanding of basic mechanisms that might not have relevance for human health. Additionally, good research ideas often do not yield the anticipated results. As long as the way in which these ideas are prioritised for research is transparent and warranted, these disappointments should not be deemed wasteful; they are simply an inevitable feature of the way science works. However, some sources of waste cannot be justified. In this report, we discuss how avoidable waste can be considered when research priorities are set. We have four recommendations. First, ways to improve the yield from basic research should be investigated. Second, the transparency of processes by which funders prioritise important uncertainties should be increased, making clear how they take account of the needs of potential users of research. Third, investment in additional research should always be preceded by systematic assessment of existing evidence. Fourth, sources of information about research that is in progress should be strengthened and developed and used by researchers. Research funders have primary responsibility for reductions in waste resulting from decisions about what research to do.

Abstract

Genetic risk assessment is becoming an important component of clinical decision-making. Genetic Risk Scores (GRSs) allow the composite assessment of genetic risk in complex traits. A technically and clinically pertinent question is how to most easily and effectively combine a GRS with an assessment of clinical risk derived from established non-genetic risk factors as well as to clearly present this information to patient and health care providers.We illustrate a means to combine a GRS with an independent assessment of clinical risk using a log-link function. We apply the method to the prediction of coronary heart disease (CHD) in the Atherosclerosis Risk in Communities (ARIC) cohort. We evaluate different constructions based on metrics of effect change, discrimination, and calibration.The addition of a GRS to a clinical risk score (CRS) improves both discrimination and calibration for CHD in ARIC. RESULTS are similar regardless of whether external vs. internal coefficients are used for the CRS, risk factor single nucleotide polymorphisms (SNPs) are included in the GRS, or subjects with diabetes at baseline are excluded. We outline how to report the construction and the performance of a GRS using our method and illustrate a means to present genetic risk information to subjects and/or their health care provider.The proposed method facilitates the standardized incorporation of a GRS in risk assessment.

Abstract

Most studies on global health inequality consider unequal health care and socio-economic conditions but neglect inequality in the production of health knowledge relevant to addressing disease burden. We demonstrate this inequality and identify likely causes. Using disability-adjusted life years (DALYs) for 111 prominent medical conditions, assessed globally and nationally by the World Health Organization, we linked DALYs with MEDLINE articles for each condition to assess the influence of DALY-based global disease burden, compared to the global market for treatment, on the production of relevant MEDLINE articles, systematic reviews, clinical trials and research using animal models vs. humans. We then explored how DALYs, wealth, and the production of research within countries correlate with this global pattern. We show that global DALYs for each condition had a small, significant negative relationship with the production of each type of MEDLINE articles for that condition. Local processes of health research appear to be behind this. Clinical trials and animal studies but not systematic reviews produced within countries were strongly guided by local DALYs. More and less developed countries had very different disease profiles and rich countries publish much more than poor countries. Accordingly, conditions common to developed countries garnered more clinical research than those common to less developed countries. Many of the health needs in less developed countries do not attract attention among developed country researchers who produce the vast majority of global health knowledge--including clinical trials--in response to their own local needs. This raises concern about the amount of knowledge relevant to poor populations deficient in their own research infrastructure. We recommend measures to address this critical dimension of global health inequality.

Abstract

Jager and Leek have tried to estimate a false-discovery rate (FDR) in abstracts of articles published in five medical journals during 2000-2010. Their approach is flawed in sampling, calculations, and conclusions. It uses a tiny portion of select papers in highly select journals. Randomized controlled trials and systematic reviews (designs with the lowest anticipated false-positive rates) are 52% of the analyzed papers, while these designs account for only 4% in PubMed in the same period. The FDR calculations consider the entire published literature as equivalent to a single genomic experiment where all performed analyses are reported without selection or distortion. However, the data used are the P-values reported in the abstracts of published papers; these P-values are a highly distorted, highly select sample. Besides selective reporting biases, all other biases, in particular confounding in observational studies, are also ignored, while these are often the main drivers for high false-positive rates in the biomedical literature. A reproducibility check of the raw data shows that much of the data Jager and Leek used are either wrong or make no sense: most of the usable data were missed by their script, 94% of the abstracts that reported ≥2 P-values had high correlation/overlap between reported outcomes, and only a minority of P-values corresponded to relevant primary outcomes. The Jager and Leek paper exemplifies the dreadful combination of using automated scripts with wrong methods and unreliable data. Sadly, this combination is common in the medical literature.

Abstract

Several commonly used medications have been associated with increased cancer risk in the literature. Here, we evaluated the strength and consistency of these claims in published meta-analyses. We carried out an umbrella review of 74 meta-analysis articles addressing the association of commonly used medications (antidiabetics, antihyperlipidemics, antihypertensives, antirheumatics, drugs for osteoporosis, and others) with cancer risk where at least one meta-analysis in the medication class included some data from randomized trials. Overall, 51 articles found no statistically significant differences, 13 found some decreased cancer risk, and 11 found some increased risk (one reported both increased and decreased risks). The 11 meta-analyses that found some increased risks reported 16 increased risk estimates, of which 5 pertained to overall cancer and 11 to site-specific cancer. Six of the 16 estimates were derived from randomized trials and 10 from observational data. Estimates of increased risk were strongly inversely correlated with the amount of evidence (number of cancer cases) (Spearman's correlation coefficient = -0.77, P < 0.001). In 4 of the 16 topics, another meta-analysis existed that was larger (n = 2) or included better controlled data (n = 2) and in all 4 cases there was no statistically significantly increased risk of malignancy. No medication or class had substantial and consistent evidence for increased risk of malignancy. However, for most medications we cannot exclude small risks or risks in population subsets. Such risks are unlikely to be possible to document robustly unless very large, collaborative studies with standardized analyses and no selective reporting are carried out.

Abstract

The allocation of research resources should favor conditions responsible for the greatest disease burden. This is particularly important in pediatric populations, which have been underrepresented in clinical research. Our aim was to measure the association between the focus of pediatric clinical trials and burden of disease and to identify neglected clinical domains.We performed a cross-sectional study of clinical trials by using trial records in ClinicalTrials.gov. All trials started in 2006 or after and studying patient-level interventions in pediatric populations were included. Age-specific measures of disease burden were obtained for 21 separate conditions for high-, middle-, and low-income countries. We measured the correlation between number of pediatric clinical trials and disease burden for each condition.Neuropsychiatric conditions and infectious diseases were the most studied conditions globally in terms of number of trials (874 and 847 trials, respectively), while intentional injuries (5 trials) and maternal conditions (4 trials) were the least studied. Clinical trials were only moderately correlated with global disease burden (r = 0.58, P = .006). Correlations were also moderate within each of the country income levels, but lowest in low-income countries (r = .47, P = .03). Globally, the conditions most understudied relative to disease burden were injuries (-260 trials for unintentional injuries and -160 trials for intentional injuries), nutritional deficiencies (-175 trials), and respiratory infections (-171 trials).Pediatric clinical trial activity is only moderately associated with pediatric burden of disease, and least associated in low-income countries. The mismatch between clinical trials and disease burden identifies key clinical areas for focus and investment.

Abstract

Abandoning ineffective medical practices and mitigating the risks of untested practices are important for improving patient health and containing healthcare costs. Historically, this process has relied on the evidence base, societal values, cultural tensions, and political sway, but not necessarily in that order. We propose a conceptual framework to guide and prioritize this process, shifting emphasis toward the principles of evidence-based medicine, acknowledging that evidence may still be misinterpreted or distorted by recalcitrant proponents of entrenched practices and other biases.

Abstract

The best validated susceptibility variants for Parkinson's disease are located in the α-synuclein (SNCA) and microtubule-associated protein tau (MAPT) genes. Recently, a protective p.N551K-R1398H-K1423K haplotype in the leucine-rich repeat kinase 2 (LRRK2) gene was identified, with p.R1398H appearing to be the most likely functional variant. To date, the consistency of the protective effect of LRRK2 p.R1398H across MAPT and SNCA variant genotypes has not been assessed. To address this, we examined 4 SNCA variants (rs181489, rs356219, rs11931074, and rs2583988), the MAPT H1-haplotype-defining variant rs1052553, and LRRK2 p.R1398H (rs7133914) in Caucasian (n = 10,322) and Asian (n = 2289) series. There was no evidence of an interaction of LRRK2 p.R1398H with MAPT or SNCA variants (all p ≥ 0.10); the protective effect of p.R1398H was observed at similar magnitude across MAPT and SNCA genotypes, and the risk effects of MAPT and SNCA variants were observed consistently for LRRK2 p.R1398H genotypes. Our results indicate that the association of LRRK2 p.R1398H with Parkinson's disease is independent of SNCA and MAPT variants, and vice versa, in Caucasian and Asian populations.

Abstract

Major depressive disorder (MDD) may be present in 10%-20% of patients in medical settings. Routine depression screening is sometimes recommended to improve depression management. However, studies of the diagnostic accuracy of depression screening tools have typically used data-driven, exploratory methods to select optimal cutoffs. Often, these studies report results from a small range of cutoff points around whatever cutoff score is most accurate in that given study. When published data are combined in meta-analyses, estimates of accuracy for different cutoff points may be based on data from different studies, rather than data from all studies for each possible cutoff point. As a result, traditional meta-analyses may generate exaggerated estimates of accuracy. Individual patient data (IPD) meta-analyses can address this problem by synthesizing data from all studies for each cutoff score to obtain diagnostic accuracy estimates. The nine-item Patient Health Questionnaire-9 (PHQ-9) and the shorter PHQ-2 and PHQ-8 are commonly recommended for depression screening. Thus, the primary objectives of our IPD meta-analyses are to determine the diagnostic accuracy of the PHQ-9, PHQ-8, and PHQ-2 to detect MDD among adults across all potentially relevant cutoff scores. Secondary analyses involve assessing accuracy accounting for patient factors that may influence accuracy (age, sex, medical comorbidity).Data sources will include MEDLINE, MEDLINE In-Process & Other Non-Indexed Citations, PsycINFO, and Web of Science. We will include studies that included a Diagnostic and Statistical Manual or International Classification of Diseases diagnosis of MDD based on a validated structured or semi-structured clinical interview administered within 2 weeks of the administration of the PHQ. Two reviewers will independently screen titles and abstracts, perform full article review, and extract study data. Disagreements will be resolved by consensus. Risk of bias will be assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Bivariate random-effects meta-analysis will be conducted for the full range of plausible cutoff values.The proposed IPD meta-analyses will allow us to obtain estimates of the diagnostic accuracy of the PHQ-9, PHQ-8, and PHQ-2.PROSPERO CRD42014010673.

Abstract

The Population Studies Research Network of Cancer Care Ontario hosted a strategic planning workshop to establish an agenda for a prevention intervention research program in Ontario, including priority topics for investigation and design considerations. The two-day workshop included: presentations on background papers developed to facilitate participants' preparation for and discussions in the workshop; keynote presentations on intervention research concerning primary prevention of chronic diseases, design and study implementation considerations; a dedicated session on critical and creative thinking to stimulate participation and discussion topics; breakout groups to identify, discuss and present study ideas, designs, implementation considerations; and a consensus process to discuss and identify recommendations for research priorities and next steps. The retreat yielded the following recommendations: 1) develop an intervention research agenda that includes working with existing large-scale cohorts; 2) develop an intervention research agenda that includes novel research designs that could target individuals or groups; and 3) develop an intervention research agenda in which studies collect data on costs, define stakeholders, and ensure clear strategies for stakeholder engagement and knowledge transfer. The Population Studies Research Network will develop options from these recommendations and release a call for proposals in 2014 for intervention research pilot projects that reflect these recommendations. Pilot projects will be evaluated based on their fit with the retreat's recommendations, and their potential to scale up to full studies and application in practice.

Abstract

The aim of this study was to investigate whether the combination of conventional pulmonary vein isolation (PVI) by circumferential antral ablation with ganglionated plexi (GP) modification in a single ablation procedure, yields higher success rates than PVI or GP ablation alone, in patients with paroxysmal atrial fibrillation (PAF).Conventional PVI transects the major left atrial GP, and it is possible that autonomic denervation by inadvertent GP ablation plays a central role in the efficacy of PVI.A total of 242 patients with symptomatic PAF were recruited and randomized as follows: 1) circumferential PVI (n = 78); 2) anatomic ablation of the main left atrial GP (n = 82); or 3) circumferential PVI followed by anatomic ablation of the main left atrial GP (n = 82). The primary endpoint was freedom from atrial fibrillation (AF) or other sustained atrial tachycardia (AT), verified by monthly visits, ambulatory electrocardiographic monitoring, and implantable loop recorders, during a 2-year follow-up period.Freedom from AF or AT was achieved in 44 (56%), 39 (48%), and 61 (74%) patients in the PVI, GP, and PVI+GP groups, respectively (p = 0.004 by log-rank test). PVI+GP ablation strategy compared with PVI alone yielded a hazard ratio of 0.53 (95% confidence interval: 0.31 to 0.91; p = 0.022) for recurrence of AF or AT. Fluoroscopy duration was 16 ± 3 min, 20 ± 5 min, and 23 ± 5 min for PVI, GP, and PVI+GP groups, respectively (p < 0.001). Post-ablation atrial flutter did not differ between groups: 5.1% in PVI, 4.9% in GP, and 6.1% in PVI+GP. No serious adverse procedure-related events were encountered.Addition of GP ablation to PVI confers a significantly higher success rate compared with either PVI or GP alone in patients with PAF.

Abstract

Many clinical trials examine a composite outcome of admission to hospital and death, or infer a relationship between hospital admission and survival benefit. This assumes concordance of the outcomes "hospital admission" and "death." However, whether the effects of a treatment on hospital admissions and readmissions correlate to its effect on serious outcomes such as death is unknown. We aimed to assess the correlation and concordance of effects of medical interventions on admission rates and mortality.We searched the Cochrane Database of Systematic Reviews from its inception to January 2012 (issue 1, 2012) for systematic reviews of treatment comparisons that included meta-analyses for both admission and mortality outcomes. For each meta-analysis, we synthesized treatment effects on admissions and death, from respective randomized trials reporting those outcomes, using random-effects models. We then measured the concordance of directions of effect sizes and the correlation of summary estimates for the 2 outcomes.We identified 61 meta-analyses including 398 trials reporting mortality and 182 trials reporting admission rates; 125 trials reported both outcomes. In 27.9% of comparisons, the point estimates of treatment effects for the 2 outcomes were in opposite directions; in 8.2% of trials, the 95% confidence intervals did not overlap. We found no significant correlation between effect sizes for admission and death (Pearson r = 0.07, p = 0.6). Our results were similar when we limited our analysis to trials reporting both outcomes.In this metaepidemiological study, admission and mortality outcomes did not correlate, and discordances occurred in about one-third of the treatment comparisons included in our analyses. Both outcomes convey useful information and should be reported separately, but extrapolating the benefits of admission to survival is unreliable and should be avoided.

Abstract

Hypertriglyceridemia (HTG) is a heritable risk factor for cardiovascular disease. Investigating the genetics of HTG may identify new drug targets. There are ~35 known single-nucleotide variants (SNVs) that explain only ~10% of variation in triglyceride (TG) level. Because of the genetic heterogeneity of HTG, a family study design is optimal for identification of rare genetic variants with large effect size because the same mutation can be observed in many relatives and cosegregation with TG can be tested. We considered HTG in a five-generation family of European American descent (n = 121), ascertained for familial combined hyperlipidemia. By using Bayesian Markov chain Monte Carlo joint oligogenic linkage and association analysis, we detected linkage to chromosomes 7 and 17. Whole-exome sequence data revealed shared, highly conserved, private missense SNVs in both SLC25A40 on chr7 and PLD2 on chr17. Jointly, these SNVs explained 49% of the genetic variance in TG; however, only the SLC25A40 SNV was significantly associated with TG (p = 0.0001). This SNV, c.374A>G, causes a highly disruptive p.Tyr125Cys substitution just outside the second helical transmembrane region of the SLC25A40 inner mitochondrial membrane transport protein. Whole-gene testing in subjects from the Exome Sequencing Project confirmed the association between TG and SLC25A40 rare, highly conserved, coding variants (p = 0.03). These results suggest a previously undescribed pathway for HTG and illustrate the power of large pedigrees in the search for rare, causal variants.

Abstract

PURPOSE: Many studies have reported on the use of narrow band imaging (NBI) colonoscopy to differentiate neoplastic from non-neoplastic colorectal polyps. It has potential to replace pathological diagnosis of diminutive polyps. We aimed to perform a systematic review and meta-analysis on the real-time diagnostic operating characteristics of NBI colonoscopy. METHODS: We searched PubMed, SCOPUS and Cochrane databases and abstracts. We used a two-level bivariate meta-analysis following a random effects model to summarise the data and fit hierarchical summary receiver-operating characteristic (HSROC) curves. The area under the HSROC curve serves as an indicator of the diagnostic test strength. We calculated summary sensitivity, specificity and negative predictive value (NPV). We assessed agreement of surveillance interval recommendations based on endoscopic diagnosis compared to pathology. RESULTS: For NBI diagnosis of colorectal polyps, the area under the HSROC curve was 0.92 (95% CI 0.90 to 0.94), based on 28 studies involving 6280 polyps in 4053 patients. The overall sensitivity was 91.0% (95% CI 87.6% to 93.5%) and specificity was 82.6% (95% CI 79.0% to 85.7%). In eight studies (n=2146 polyps) that used high-confidence diagnostic predictions, sensitivity was 93.8% and specificity was 83.3%. The NPVs exceeded 90% when 60% or less of all polyps were neoplastic. Surveillance intervals based on endoscopic diagnosis agreed with those based on pathology in 92.6% of patients (95% CI 87.9% to 96.3%). CONCLUSIONS: NBI diagnosis of colorectal polyps is highly accurate-the area under the HSROC curve exceeds 0.90. High-confidence predictions provide >90% sensitivity and NPV. It shows high potential for real-time endoscopic diagnosis.

Abstract

During the last two decades, epidemiology has undergone a rapid evolution toward collaborative research. The proliferation of multi-institutional, interdisciplinary consortia has acquired particular prominence in cancer research. Herein, we describe the characteristics of a network of 49 established cancer epidemiology consortia (CEC) currently supported by the Epidemiology and Genomics Research Program (EGRP) at the National Cancer Institute (NCI). This collection represents the largest disease-based research network for collaborative cancer research established in population sciences. We describe the funding trends, geographic distribution, and areas of research focus. The CEC have been partially supported by 201 grants and yielded 3,876 publications between 1995 and 2011. We describe this output in terms of interdisciplinary collaboration and translational evolution. We discuss challenges and future opportunities in the establishment and conduct of large-scale team science within the framework of CEC, review future prospects for this approach to large-scale, interdisciplinary cancer research, and describe a model for the evolution of an integrated Network of Cancer Consortia optimally suited to address and support 21st-century epidemiology.

Abstract

We studied the entire agenda of randomized clinical trials in pulmonary hypertension (PH) using sociological methods. We explored the geometry of the PH network to interpret the evidence on multiple competing treatments for the same indication.We searched MEDLINE, Embase and Cochrane Library Databases for published studies. We queried clinicaltrials.gov and WHO International Clinical Trials Registry platform for non-published studies.We found 75 randomized trials (41 published [n = 4136 participants] and 34 registered unpublished [planned n = 3470 participants]). Of the published randomized studies, all used placebo as the comparator arm except for two nonindustry-sponsored comparisons between phosphodiestearase-5 (PDE-5) inhibitors and endothelin receptor antagonists (ERA), and one study comparing two different regimens of treprostinil. Similarly, only five unpublished/ongoing trials used an active PH treatment as comparator (PDE-5 inhibitors versus ERA (n = 3), different doses of sildenafil (n = 1) and two formulations of epoprostenol (n = 1). Of the 75 trials, 47 were sponsored by the manufacturer of the tested active product(s), and only two trials were sponsored by two companies comparing their products.The relative merits of different treatment options are not directly known, as there are very few head-to-head comparisons. A limited number of ongoing studies are using active FDA-approved PH-treatments for comparison. This lack of information can be overcome by carefully designing comparative effectiveness trials.

Abstract

We have generated a list of highly influential biomedical researchers based on Scopus citation data from the period 1996-2011. Of the 15,153,100 author identifiers in Scopus, approximately 1% (n=149,655) have an h-index >=20. Of those, we selected 532 authors who belonged to the 400 with highest total citation count (>=25,142 citations) and/or the 400 with highest h-index (>=76). Of those, we selected the top-400 living core biomedical researchers based on a normalized score combining total citations and h-index. Another 62 authors whose focus is outside biomedicine had a normalized score that was at least as high as the score of the 400th core biomedical researcher. We provide information on the profile of these most influential authors, including the most common Medical Subject Heading terms in their articles that are also specific to their work, most common journals where they publish, number of papers with over 100 citations that they have published as first/single, last, or middle authors, and impact score adjusted for authorship positions, given that crude citation indices and authorship positions are almost totally orthogonal. We also show for each researcher the distribution of their papers across 4 main levels (basic-to-applied) of research. We discuss technical issues, limitations and caveats, comparisons against other lists of highly-cited researchers, and potential uses of this resource.

Abstract

A billion deaths from tobacco are expected by 2100. Many policy interventions such as increased taxation, restrictions on advertisement, smoking bans, as well as behavioral interventions, such as pharmacological and psychological treatments for smoking cessation, decrease tobacco use, but they reach their limits. Endgame scenarios focusing on tobacco supply rather than demand are increasingly discussed, but meet with resistance by the industry and even by many tobacco control experts. A main stumbling block that requires more attention is what to do with the tobacco industry in endgame scenarios. This industry has employed notoriously talented experts in law, business, organization, marketing, advertising, strategy, policy, and statistics and has tremendous lobbying power. Performance-based regulatory approaches can pose a legal obligation on manufacturers to decrease - and eventually - eliminate tobacco products according to specified schedules. Penalties and rewards can make such plans both beneficial for public health and attractive to the companies that do the job well. We discuss caveats and reality checks of engaging the tobacco industry to eliminate its current market and change focus. Brainstorming is warranted to entice the industry to abandon tobacco for other profit goals. To get the dialogue started, we propose the wild possibility of hiring former tobacco companies to reduce the costs of healthcare, thereby addressing concurrently two major challenges to public health.

Abstract

Environmental and behavioural factors are thought to contribute to all-cause mortality. Here, we develop a method to systematically screen and validate the potential independent contributions to all-cause mortality of 249 environmental and behavioural factors in the National Health and Nutrition Examination Survey (NHANES).We used Cox proportional hazards regression to associate 249 factors with all-cause mortality while adjusting for sociodemographic factors on data in the 1999-2000 and 2001-02 surveys (median 5.5 follow-up years). We controlled for multiple comparisons with the false discovery rate (FDR) and validated significant findings in the 2003-04 survey (median 2.8 follow-up years). We selected 249 factors from a set of all possible factors based on their presence in both the 1999-2002 and 2003-04 surveys and linkage with at least 20 deceased participants. We evaluated the correlation pattern of validated factors and built a multivariable model to identify their independent contribution to mortality.We identified seven environmental and behavioural factors associated with all-cause mortality, including serum and urinary cadmium, serum lycopene levels, smoking (3-level factor) and physical activity. In a multivariable model, only physical activity, past smoking, smoking in participant's home and lycopene were independently associated with mortality. These three factors explained 2.1% of the variance of all-cause mortality after adjusting for demographic and socio-economic factors.Our association study suggests that, of the set of 249 factors in NHANES, physical activity, smoking, serum lycopene and serum/urinary cadmium are associated with all-cause mortality as identified in previous studies and after controlling for multiple hypotheses and validation in an independent survey. Whereas other NHANES factors may be associated with mortality, they may require larger cohorts with longer time of follow-up to detect. It is possible to use a systematic association study to prioritize risk factors for further investigation.

Abstract

Making raw data from clinical trials widely publically available should reduce selective reporting biases and enhance the reproducibility of and trust in clinical research. The optimal procedures for data sharing are hotly debated. Some of the caveats and limitations in proposed data-sharing policies are potentially restrictive, and we argue in favor of more widespread availability of data from clinical research.

Abstract

Morbidity and mortality from preventable, non-communicable chronic disease (NCD) threatens the health of our populations and our economies. The accumulation of vast amounts of scientific knowledge has done little to change this. New and innovative thinking is essential to foster new creative approaches that leverage and integrate evidence through the support of big data, technology, and design thinking. The purpose of this paper is to summarize the results of a consensus meeting on NCD prevention sponsored by the International Olympic Committee (IOC) in April, 2013. Within the context of advocacy for multifaceted systems change, the IOC's focus is to create solutions that gain traction within health care systems. The group of participants attending the meeting achieved consensus on a strategy for the prevention and management of chronic disease that includes the following: 1. Focus on behavioural change as the core component of all clinical programs for the prevention and management of chronic disease. 2. Establish actual centres to design, implement, study, and improve preventive programs for chronic disease. 3. Use human-centered design in the creation of prevention programs with an inclination to action, rapid prototyping and multiple iterations. 4. Extend the knowledge and skills of Sports and Exercise Medicine (SEM) professionals to build new programs for the prevention and treatment of chronic disease focused on physical activity, diet and lifestyle. 5. Mobilize resources and leverage networks to scale and distribute programs of prevention. True innovation lies in the ability to align thinking around these core strategies to ensure successful implementation of NCD prevention and management programs within health care. The IOC and SEM community are in an ideal position to lead this disruptive change. The outcome of the consensus meeting was the creation of the IOC Non-Communicable Diseases ad-hoc Working Group charged with the responsibility of moving this agenda forward.

Abstract

Anti-TNF agents and other biologic therapies are widely prescribed for a variety of indications, with total sales that exceed $200 billion to date. In rheumatic diseases, biologic agents have now been studied in more than 200 randomized clinical trials and over 100 subsequent meta-analyses; however, the information obtained does not always meet the needs of patients and clinicians. In this Review, we discuss the current issues concerning the evidence derived from such studies: potential biases favouring positive results; a paucity of head-to-head comparisons between biologically active agents; overwhelming involvement of manufacturer sponsors in trials and in the synthesis of the evidence; the preference for trials with limited follow-up; and the potential for spurious findings on adverse events, leading to endless debates about malignancy risk. We debate the responsibilities of regulatory authorities, the pharmaceutical industry and academia in attempting to solve these shortcomings and challenges. We propose that improvements in the evidence regarding biologic treatments that are continually being added to the therapeutic armamentarium for rheumatic diseases might require revisiting the design and conduct of studies. For example, trials with long-term follow-up that are independent of the pharmaceutical industry, head-to-head comparisons of therapeutic agents and the use of rigorous clinical outcomes should be considered, and public availability of raw data endorsed.

Abstract

Morbidity and mortality from preventable, non-communicable chronic disease (NCD) threatens the health of our populations and our economies. The accumulation of vast amounts of scientific knowledge has done little to change this. New and innovative thinking is essential to foster new creative approaches that leverage and integrate evidence through the support of big data, technology and design thinking. The purpose of this paper is to summarise the results of a consensus meeting on NCD prevention sponsored by the IOC in April 2013. Within the context of advocacy for multifaceted systems change, the IOC's focus is to create solutions that gain traction within healthcare systems. The group of participants attending the meeting achieved consensus on a strategy for the prevention and management of chronic disease that includes the following: (1) Focus on behavioural change as the core component of all clinical programmes for the prevention and management of chronic disease. (2) Establish actual centres to design, implement, study and improve preventive programmes for chronic disease. (3) Use human-centred design in the creation of prevention programmes with an inclination to action, rapid prototyping and multiple iterations. (4) Extend the knowledge and skills of Sports and Exercise Medicine (SEM) professionals to build new programmes for the prevention and treatment of chronic disease focused on physical activity, diet and lifestyle. (5) Mobilise resources and leverage networks to scale and distribute programmes of prevention. True innovation lies in the ability to align thinking around these core strategies to ensure successful implementation of NCD prevention and management programmes within healthcare. The IOC and SEM community are in an ideal position to lead this disruptive change. The outcome of the consensus meeting was the creation of the IOC Non-Communicable Diseases ad hoc Working Group charged with the responsibility of moving this agenda forward.

Abstract

Mental disorders are associated with premature mortality, and the magnitudes of risk have commonly been estimated using hospital data. However, psychiatric patients who are hospitalized have more severe illness and do not adequately represent mental disorders in the general population. We conducted a national cohort study using outpatient and inpatient diagnoses for the entire Swedish adult population (N = 7,253,516) to examine the extent to which mortality risks are overestimated using inpatient diagnoses only. Outcomes were all-cause and suicide mortality during 8 years of follow-up (2001-2008). There were 377,339 (5.2%) persons with any inpatient psychiatric diagnosis, vs. 680,596 (9.4%) with any inpatient or outpatient diagnosis, hence 44.6% of diagnoses were missed using inpatient data only. When including and accounting for prevalent psychiatric cases, all-cause mortality risk among persons with any mental disorder was overestimated by 15.3% using only inpatient diagnoses (adjusted hazard ratio [aHR], 5.89; 95% CI, 5.85-5.92) vs. both inpatient and outpatient diagnoses (aHR, 5.11; 95% CI, 5.08-5.14). Suicide risk was overestimated by 18.5% (aHRs, 23.91 vs. 20.18), but this varied widely by specific disorders, from 4.4% for substance use to 49.1% for anxiety disorders. The sole use of inpatient diagnoses resulted in even greater overestimation of all-cause or suicide mortality risks when prevalent cases were unidentified (∼20-30%) or excluded (∼25-40%). However, different methods for handling prevalent cases resulted in only modest variation in risk estimates when using both inpatient and outpatient diagnoses. These findings have important implications for the interpretation of hospital-based studies and the design of future studies.

Abstract

Smoking influences body weight such that smokers weigh less than non-smokers and smoking cessation often leads to weight increase. The relationship between body weight and smoking is partly explained by the effect of nicotine on appetite and metabolism. However, the brain reward system is involved in the control of the intake of both food and tobacco. We evaluated the effect of single-nucleotide polymorphisms (SNPs) affecting body mass index (BMI) on smoking behavior, and tested the 32 SNPs identified in a meta-analysis for association with two smoking phenotypes, smoking initiation (SI) and the number of cigarettes smoked per day (CPD) in an Icelandic sample (N=34,216 smokers). Combined according to their effect on BMI, the SNPs correlate with both SI (r=0.019, P=0.00054) and CPD (r=0.032, P=8.0 × 10(-7)). These findings replicate in a second large data set (N=127,274, thereof 76,242 smokers) for both SI (P=1.2 × 10(-5)) and CPD (P=9.3 × 10(-5)). Notably, the variant most strongly associated with BMI (rs1558902-A in FTO) did not associate with smoking behavior. The association with smoking behavior is not due to the effect of the SNPs on BMI. Our results strongly point to a common biological basis of the regulation of our appetite for tobacco and food, and thus the vulnerability to nicotine addiction and obesity.

Abstract

To determine the comparative effectiveness of exercise versus drug interventions on mortality outcomes.Metaepidemiological study.Meta-analyses of randomised controlled trials with mortality outcomes comparing the effectiveness of exercise and drug interventions with each other or with control (placebo or usual care).Medline and Cochrane Database of Systematic Reviews, May 2013.Mortality.We combined study level death outcomes from exercise and drug trials using random effects network meta-analysis.We included 16 (four exercise and 12 drug) meta-analyses. Incorporating an additional three recent exercise trials, our review collectively included 305 randomised controlled trials with 339,274 participants. Across all four conditions with evidence on the effectiveness of exercise on mortality outcomes (secondary prevention of coronary heart disease, rehabilitation of stroke, treatment of heart failure, prevention of diabetes), 14,716 participants were randomised to physical activity interventions in 57 trials. No statistically detectable differences were evident between exercise and drug interventions in the secondary prevention of coronary heart disease and prediabetes. Physical activity interventions were more effective than drug treatment among patients with stroke (odds ratios, exercise v anticoagulants 0.09, 95% credible intervals 0.01 to 0.70 and exercise v antiplatelets 0.10, 0.01 to 0.62). Diuretics were more effective than exercise in heart failure (exercise v diuretics 4.11, 1.17 to 24.76). Inconsistency between direct and indirect comparisons was not significant.Although limited in quantity, existing randomised trial evidence on exercise interventions suggests that exercise and many drug interventions are often potentially similar in terms of their mortality benefits in the secondary prevention of coronary heart disease, rehabilitation after stroke, treatment of heart failure, and prevention of diabetes.

Abstract

It is common practice in genome-wide association studies (GWAS) to focus on the relationship between disease risk and genetic variants one marker at a time. When relevant genes are identified it is often possible to implicate biological intermediates and pathways likely to be involved in disease aetiology. However, single genetic variants typically explain small amounts of disease risk. Our idea is to construct allelic scores that explain greater proportions of the variance in biological intermediates, and subsequently use these scores to data mine GWAS. To investigate the approach's properties, we indexed three biological intermediates where the results of large GWAS meta-analyses were available: body mass index, C-reactive protein and low density lipoprotein levels. We generated allelic scores in the Avon Longitudinal Study of Parents and Children, and in publicly available data from the first Wellcome Trust Case Control Consortium. We compared the explanatory ability of allelic scores in terms of their capacity to proxy for the intermediate of interest, and the extent to which they associated with disease. We found that allelic scores derived from known variants and allelic scores derived from hundreds of thousands of genetic markers explained significant portions of the variance in biological intermediates of interest, and many of these scores showed expected correlations with disease. Genome-wide allelic scores however tended to lack specificity suggesting that they should be used with caution and perhaps only to proxy biological intermediates for which there are no known individual variants. Power calculations confirm the feasibility of extending our strategy to the analysis of tens of thousands of molecular phenotypes in large genome-wide meta-analyses. We conclude that our method represents a simple way in which potentially tens of thousands of molecular phenotypes could be screened for causal relationships with disease without having to expensively measure these variables in individual disease collections.

US studies may overestimate effect sizes in softer researchPROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICAFanelli, D., Ioannidis, J. P.2013; 110 (37): 15031-15036

Abstract

Many biases affect scientific research, causing a waste of resources, posing a threat to human health, and hampering scientific progress. These problems are hypothesized to be worsened by lack of consensus on theories and methods, by selective publication processes, and by career systems too heavily oriented toward productivity, such as those adopted in the United States (US). Here, we extracted 1,174 primary outcomes appearing in 82 meta-analyses published in health-related biological and behavioral research sampled from the Web of Science categories Genetics & Heredity and Psychiatry and measured how individual results deviated from the overall summary effect size within their respective meta-analysis. We found that primary studies whose outcome included behavioral parameters were generally more likely to report extreme effects, and those with a corresponding author based in the US were more likely to deviate in the direction predicted by their experimental hypotheses, particularly when their outcome did not include additional biological parameters. Nonbehavioral studies showed no such "US effect" and were subject mainly to sampling variance and small-study effects, which were stronger for non-US countries. Although this latter finding could be interpreted as a publication bias against non-US authors, the US effect observed in behavioral research is unlikely to be generated by editorial biases. Behavioral studies have lower methodological consensus and higher noise, making US researchers potentially more likely to express an underlying propensity to report strong and significant findings.

Abstract

To examine whether the exclusion of individual treatment comparators, including placebo/no treatment, affects the results of network meta-analysis.Survey of networks with individual trial data.PubMed and communication with authors of network meta-analyses.We included networks that had five or more treatments, contained at least two closed loops, had at least twice as many studies as treatments, and had trial level data available. Investigators abstracted information about study design, participants, outcomes, network geometry, and the exclusion of eligible treatments.Among 18 eligible networks involving 757 randomised controlled trials with 750 possible treatment comparisons, 11 had upfront decided not to consider all treatment comparators and only 10 included placebo/no treatment nodes. In 7/18 networks, there was at least one node whose removal caused a more than 1.10-fold average relative change in the estimated treatments effects, and switches in the top three treatments were observed in 9/18 networks. Removal of placebo/no treatment caused large relative changes of the treatment effects (average change 1.16-3.10-fold) for four of the 10 networks that had originally included placebo/no treatment nodes. Exclusion of current uncommonly used drugs resulted in substantial changes of the treatment effects (average 1.21-fold) in one of three networks on systemic treatments for advanced malignancies.Excluding treatments in network meta-analyses sometimes can have important effects on their results and can diminish the usefulness of the research to clinicians if important comparisons are missing.

Abstract

In a systematic review and random-effects meta-analysis, we evaluated whether obesity is associated with postoperative atrial fibrillation (POAF) in patients undergoing cardiac operations. We selected 18 observational studies until December 2011 that excluded patients with preoperative AF (n=36,147). Obese patients had a modest higher risk of POAF compared with nonobese (odds ratio, 1.12; 95% confidence interval, 1.04 to 1.21; p=0.002). The association between obesity and POAF did not vary substantially by type of cardiac operation, study design, or year of publication. POAF was significantly associated with a higher risk of stroke, respiratory failure, and operative death.

Abstract

OBJECTIVE: To model how to select the optimal pair of type I and type II errors that maximize study value when there are constrains on the available study sample size. STUDY DESIGN AND SETTING: Correct inferences [true positives (TPs) and true negatives (TNs)] increase and wrong inferences (false positives and false negatives) decrease the value of a study. We model the composite value of a study based on these four inferences, their relative importance, and relative frequency using multiplicative and additive models. Numerical examples are presented for randomized trials, epidemiologic studies, and agnostic omics investigations with massive testing and variable sample size constraints. RESULTS: The optimal choice of type I and type II errors varies a lot according to the available sample size and the plausible effect sizes in each field. We show how equations can be streamlined for special applications: when the value of all four inferences is considered equal, when the identification of TNs carries no value, and when a study carries no value unless at least one TP is discovered. CONCLUSION: The proposed optimization equations can be used to guide the selection of the optimal type I and type II errors of future studies in which sample size is constrained.

To Replicate or Not to Replicate: The Case of Pharmacogenetic Studies Have Pharmacogenomics Failed, or Do They Just Need Larger-Scale Evidence and More Replication?CIRCULATION-CARDIOVASCULAR GENETICSIoannidis, J. P.2013; 6 (4): 413-418

Abstract

Some risk exposures, including many medical and surgical procedures, typically carry hazards of death that are difficult to convey and appreciate in absolute terms. I propose presenting the death risk as a condensed life experience (i.e., the equivalent amount of life T that would carry the same cumulative mortality hazard for a person of the same age and sex based on life tables). For example, if the risk of death during an elective 1-hour procedure is 0.01%, and same-age and same-sex people have a 0.01% death risk over 1 month, one can inform the patient that "this procedure carries the same death risk as living 1 month of normal life." Comparative standards from other risky activities or from a person with the same disease at the same stage and same predictive profile could also be used. A complementary metric that may be useful to consider is the death intensity. The death intensity λ is the hazard function that shows the fold-risk estimate of dying compared with the reference person. The death intensity can vary substantially for different phases of the event, operation, or procedure (e.g., intraoperative, early postoperative, late postoperative), and this variability may also be useful to convey. T will vary depending on the time window for which it is computed. I present examples for calculating T and λ using literature data on accidents, ascent to Mount Everest, and medical and surgical procedures.

Abstract

Functional magnetic resonance imaging (fMRI) studies have reported multiple activation foci associated with a variety of conditions, stimuli or tasks. However, most of these studies used fewer than 40 participants.After extracting data (number of subjects, condition studied, number of foci identified and threshold) from 94 brain fMRI meta-analyses (k = 1,788 unique datasets) published through December of 2011, we analyzed the correlation between individual study sample sizes and number of significant foci reported. We also performed an analysis where we evaluated each meta-analysis to test whether there was a correlation between the sample size of the meta-analysis and the number of foci that it had identified. Correlation coefficients were then combined across all meta-analyses to obtain a summary correlation coefficient with a fixed effects model and we combine correlation coefficients, using a Fisher's z transformation.There was no correlation between sample size and the number of foci reported in single studies (r = 0.0050) but there was a strong correlation between sample size and number of foci in meta-analyses (r = 0.62, p<0.001). Only studies with sample sizes <45 identified larger (>40) numbers of foci and claimed as many discovered foci as studies with sample sizes ≥45, whereas meta-analyses yielded a limited number of foci relative to the yield that would be anticipated from smaller single studies.These results are consistent with possible reporting biases affecting small fMRI studies and suggest the need to promote standardized large-scale evidence in this field. It may also be that small studies may be analyzed and reported in ways that may generate a larger number of claimed foci or that small fMRI studies with inconclusive, null, or not very promising results may not be published at all.

Abstract

To assess adjustment practices for primary outcomes of randomized controlled trials and their impact on the results.Meta-epidemiologic study.25 biomedical journals with the highest impact factor according to Journal Citation Reports 2009.Randomized controlled trials published in print in 2009 that reported primary outcomes. The search yielded 684 eligible papers of randomized controlled trials, of which 200 were randomly selected.Two researchers independently extracted data on study population, intervention, primary outcome, and the adjustment plan for primary outcomes. They also recorded the magnitude and statistical significance of the intervention effect with and without adjustments, and estimated whether adjustment made a difference in the level of nominal significance. They also compared the analysis plan for model adjustment in the published trial versus the trial protocol with information on the protocol collected from registries, design papers, and communication with all corresponding authors.54% of the trials used stratified randomization, 96% presented baseline characteristics in the compared arms, and 46% also evaluated differences in baseline factors with statistical testing. Half of the trials performed adjusted analyses for the main outcome, as the sole analysis (29%) or along with unadjusted analyses (21%). Adjustment for stratification variables and for baseline variables was performed in 39% (42/108) and 42% (84/199) of the trials, respectively. Among 40 comparisons with both adjusted and unadjusted analyses, 43% had statistically significant effects, 40% had non-significant effects, and 18% had significant effects with only one of the two analyses, but not with the other. Information on analysis plan regarding model adjustment was available in 6% (9/162) of trial registry entries, 78% (21/27) of design papers, and 74% (40/54) of protocols obtained from authors. The analysis plan disagreed between the published trial and the registry, protocol, or design paper in 47% (28/60) of the studies.There is large diversity on whether and how analyses of primary outcomes are adjusted in randomized controlled trials and these choices can sometimes change the nominal significance of the results. Registered protocols should explicitly specify adjustments plans for main outcomes and analysis should follow these plans.

Abstract

Animal studies generate valuable hypotheses that lead to the conduct of preventive or therapeutic clinical trials. We assessed whether there is evidence for excess statistical significance in results of animal studies on neurological disorders, suggesting biases. We used data from meta-analyses of interventions deposited in Collaborative Approach to Meta-Analysis and Review of Animal Data in Experimental Studies (CAMARADES). The number of observed studies with statistically significant results (O) was compared with the expected number (E), based on the statistical power of each study under different assumptions for the plausible effect size. We assessed 4,445 datasets synthesized in 160 meta-analyses on Alzheimer disease (n = 2), experimental autoimmune encephalomyelitis (n = 34), focal ischemia (n = 16), intracerebral hemorrhage (n = 61), Parkinson disease (n = 45), and spinal cord injury (n = 2). 112 meta-analyses (70%) found nominally (p≤0.05) statistically significant summary fixed effects. Assuming the effect size in the most precise study to be a plausible effect, 919 out of 4,445 nominally significant results were expected versus 1,719 observed (p<10(-9)). Excess significance was present across all neurological disorders, in all subgroups defined by methodological characteristics, and also according to alternative plausible effects. Asymmetry tests also showed evidence of small-study effects in 74 (46%) meta-analyses. Significantly effective interventions with more than 500 animals, and no hints of bias were seen in eight (5%) meta-analyses. Overall, there are too many animal studies with statistically significant results in the literature of neurological disorders. This observation suggests strong biases, with selective analysis and outcome reporting biases being plausible explanations, and provides novel evidence on how these biases might influence the whole research domain of neurological animal literature.

Abstract

Meta-analysis of genome-wide association studies (GWASs) has become a popular method for discovering genetic risk variants. Here, we overview both widely applied and newer statistical methods for GWAS meta-analysis, including issues of interpretation and assessment of sources of heterogeneity. We also discuss extensions of these meta-analysis methods to complex data. Where possible, we provide guidelines for researchers who are planning to use these methods. Furthermore, we address special issues that may arise for meta-analysis of sequencing data and rare variants. Finally, we discuss challenges and solutions surrounding the goals of making meta-analysis data publicly available and building powerful consortia.

Abstract

There are no analyses of citations to books on epidemiological and statistical methods in the biomedical literature. Such analyses may shed light on how concepts and methods changed while biomedical research evolved. Our aim was to analyze the number and time trends of citations received from biomedical articles by books on epidemiological and statistical methods, and related disciplines.The data source was the Web of Science. The study books were published between 1957 and 2010. The first year of publication of the citing articles was 1945. We identified 125 books that received at least 25 citations. Books first published in 1980-1989 had the highest total and median number of citations per year. Nine of the 10 most cited texts focused on statistical methods. Hosmer & Lemeshow's Applied logistic regression received the highest number of citations and highest average annual rate. It was followed by books by Fleiss, Armitage, et al., Rothman, et al., and Kalbfleisch and Prentice. Fifth in citations per year was Sackett, et al., Evidence-based medicine. The rise of multivariate methods, clinical epidemiology, or nutritional epidemiology was reflected in the citation trends. Educational textbooks, practice-oriented books, books on epidemiological substantive knowledge, and on theory and health policies were much less cited. None of the 25 top-cited books had the theoretical or sociopolitical scope of works by Cochrane, McKeown, Rose, or Morris.Books were mainly cited to reference methods. Books first published in the 1980s continue to be most influential. Older books on theory and policies were rooted in societal and general medical concerns, while the most modern books are almost purely on methods.

Abstract

Diseases such as type 2 diabetes (T2D) result from environmental and genetic factors, and risk varies considerably in the population. T2D-related genetic loci discovered to date explain only a small portion of the T2D heritability. Some heritability may be due to gene-environment interactions. However, documenting these interactions has been difficult due to low availability of concurrent genetic and environmental measures, selection bias, and challenges in controlling for multiple hypothesis testing. Through genome-wide association studies (GWAS), investigators have identified over 90 single nucleotide polymorphisms (SNPs) associated to T2D. Using a method analogous to GWAS [environment-wide association study (EWAS)], we found five environmental factors associated with the disease. By focusing on risk factors that emerge from GWAS and EWAS, it is possible to overcome difficulties in uncovering gene-environment interactions. Using data from the National Health and Nutrition Examination Survey (NHANES), we screened 18 SNPs and 5 serum-based environmental factors for interaction in association to T2D. We controlled for multiple hypotheses using false discovery rate (FDR) and Bonferroni correction and found four interactions with FDR <20 %. The interaction between rs13266634 (SLC30A8) and trans-?-carotene withstood Bonferroni correction (corrected p = 0.006, FDR <1.5 %). The per-risk-allele effect sizes in subjects with low levels of trans-?-carotene were 40 % greater than the marginal effect size [odds ratio (OR) 1.8, 95 % CI 1.3-2.6]. We hypothesize that impaired function driven by rs13266634 increases T2D risk when combined with serum levels of nutrients. Unbiased consideration of environmental and genetic factors may help identify larger and more relevant effect sizes for disease associations.

Abstract

Expenditure on industry products (mostly drugs and devices) has spiraled over the last 15 years and accounts for substantial part of healthcare expenditure. The enormous financial interests involved in the development and marketing of drugs and devices may have given excessive power to these industries to influence medical research, policy, and practice.Review of the literature and analysis of the multiple pathways through which the industry has directly or indirectly infiltrated the broader healthcare systems. We present the analysis of the industry influences at the following levels: (i) evidence base production, (ii) evidence synthesis, (iii) understanding of safety and harms issues, (iv) cost-effectiveness evaluation, (v) clinical practice guidelines formation, (vi) healthcare professional education, (vii) healthcare practice, (viii) healthcare consumer's decisions.We located abundance of consistent evidence demonstrating that the industry has created means to intervene in all steps of the processes that determine healthcare research, strategy, expenditure, practice and education. As a result of these interferences, the benefits of drugs and other products are often exaggerated and their potential harms are downplayed, and clinical guidelines, medical practice, and healthcare expenditure decisions are biased.To serve its interests, the industry masterfully influences evidence base production, evidence synthesis, understanding of harms issues, cost-effectiveness evaluations, clinical practice guidelines and healthcare professional education and also exerts direct influences on professional decisions and health consumers. There is an urgent need for regulation and other action towards redefining the mission of medicine towards a more objective and patient-, population- and society-benefit direction that is free from conflict of interests.

Abstract

The aim of this study was to assess the role of known risk factors and specifically evaluate the role of pancreatitis potentially associated drugs as potential risk factors for post-endoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP).This was a prospective, single-center cohort study conducted in a tertiary university hospital. All eligible ERCP procedures within a 16-month period were evaluated, and all interventions, patient characteristics, and medications used were documented. The association of potential risk factor with PEP was investigated with univariable analyses. Those statistically significant were entered in a multivariable regression model.Three hundred eighteen ERCP procedures were studied. Post-ERCP pancreatitis occurred in 28 patients (8.8%). Twenty-three potential risk factors were studied in univariable analyses, and 3 of them were found to be nominally statistically significant. These 3 factors were independently associated with PEP in the multivariable model and included the use of pancreatitis potentially associated drugs, belonging to Badalov classes I or II, during the last month before ERCP (odds ratio [OR], 4.39; 95% confidence interval [CI], 1.70-5.47; P = 0.003), more than 1 guide-wire insertions in the pancreatic duct (OR, 5.00; 95% CI, 1.97-12.81; P = 0.001) and bile duct stone extraction (OR, 0.12; CI, 0.05-0.32; P < 0.001).Pancreatitis potentially associated drugs used before ERCP seem to increase the risk for PEP.

Abstract

A study with low statistical power has a reduced chance of detecting a true effect, but it is less well appreciated that low power also reduces the likelihood that a statistically significant result reflects a true effect. Here, we show that the average statistical power of studies in the neurosciences is very low. The consequences of this include overestimates of effect size and low reproducibility of results. There are also ethical dimensions to this problem, as unreliable research is inefficient and wasteful. Improving reproducibility in neuroscience is a key priority and requires attention to well-established but often ignored methodological principles.

Abstract

IMPORTANCE Numerous cardiovascular biomarkers are proposed as potential predictors of cardiovascular risk. OBJECTIVE To evaluate whether there is evidence for biases favoring statistically significant results and inflating associations in this literature. DESIGN AND SETTING PubMed search for meta-analyses of cardiovascular biomarkers that are not part of the Framingham Risk Score. MAIN OUTCOME MEASURES We estimated summary effects and between-study heterogeneity (considered "very large" for I2 > 75%). We evaluated whether large studies had significantly more conservative results than smaller studies (small-study effects) and whether there were too many studies with statistically significant results compared with what would be expected on the basis of the findings of the largest study in each meta-analysis. RESULTS Of 56 eligible meta-analyses, 49 had statistically significant results. Very large heterogeneity and small-study effects were seen in 9 and 13 meta-analyses, respectively. In 29 meta-analyses (52%), there was a significant excess of studies with statistically significant results. Only 13 of the statistically significant meta-analyses had more than 1000 cases and no hints of large heterogeneity, small-study effects, or excess significance. These included the associations of glomerular filtration rate and albumin to creatinine ratio in general and high-risk populations with cardiovascular disease mortality and of non-high-density lipoprotein cholesterol, serum albumin, Chlamydia pneumoniae IgG, glycosylated hemoglobin, nonfasting insulin, apolipoprotein B/AI ratio, erythrocyte sedimentation rate, and lipoprotein-associated phospholipase mass or activity with coronary heart disease. CONCLUSIONS AND RELEVANCE Selective reporting biases may be common in the evidence on emerging cardiovascular biomarkers. Most of the proposed associations of these biomarkers may be inflated.

Abstract

Age-related macular degeneration (AMD) is a common cause of blindness in older individuals. To accelerate the understanding of AMD biology and help design new therapies, we executed a collaborative genome-wide association study, including >17,100 advanced AMD cases and >60,000 controls of European and Asian ancestry. We identified 19 loci associated at P < 5 × 10(-8). These loci show enrichment for genes involved in the regulation of complement activity, lipid metabolism, extracellular matrix remodeling and angiogenesis. Our results include seven loci with associations reaching P < 5 × 10(-8) for the first time, near the genes COL8A1-FILIP1L, IER3-DDR1, SLC16A8, TGFBR1, RAD51B, ADAMTS9 and B3GALTL. A genetic risk score combining SNP genotypes from all loci showed similar ability to distinguish cases and controls in all samples examined. Our findings provide new directions for biological, genetic and therapeutic studies of AMD.

Abstract

Mass spectrometry platforms have attracted a lot of interest in the last 2 decades as profiling tools for native peptides and proteins with clinical potential. However, limitations associated with reproducibility and analytical robustness, especially pronounced with the initial SELDI systems, hindered the application of such platforms in biomarker qualification and clinical implementation. The scope of this article is to give a short overview on data available on performance and on analytical robustness of the different platforms for peptide profiling. Using the CE-MS platform as a paradigm, data on analytical performance are described including reproducibility (short-term and intermediate repeatability), stability, interference, quantification capabilities (limits of detection), and inter-laboratory variability. We discuss these issues by using as an example our experience with the development of a 273-peptide marker for chronic kidney disease. Finally, we discuss pros and cons and means for improvement and emphasize the need to test in terms of comparative clinical performance and impact, different platforms that pass reasonably well analytical validation tests.

Abstract

In 2012, the National Cancer Institute (NCI) engaged the scientific community to provide a vision for cancer epidemiology in the 21st century. Eight overarching thematic recommendations, with proposed corresponding actions for consideration by funding agencies, professional societies, and the research community emerged from the collective intellectual discourse. The themes are (i) extending the reach of epidemiology beyond discovery and etiologic research to include multilevel analysis, intervention evaluation, implementation, and outcomes research; (ii) transforming the practice of epidemiology by moving toward more access and sharing of protocols, data, metadata, and specimens to foster collaboration, to ensure reproducibility and replication, and accelerate translation; (iii) expanding cohort studies to collect exposure, clinical, and other information across the life course and examining multiple health-related endpoints; (iv) developing and validating reliable methods and technologies to quantify exposures and outcomes on a massive scale, and to assess concomitantly the role of multiple factors in complex diseases; (v) integrating "big data" science into the practice of epidemiology; (vi) expanding knowledge integration to drive research, policy, and practice; (vii) transforming training of 21st century epidemiologists to address interdisciplinary and translational research; and (viii) optimizing the use of resources and infrastructure for epidemiologic studies. These recommendations can transform cancer epidemiology and the field of epidemiology, in general, by enhancing transparency, interdisciplinary collaboration, and strategic applications of new technologies. They should lay a strong scientific foundation for accelerated translation of scientific discoveries into individual and population health benefits.

Abstract

To assess the extent to which meta-analysis publications of drugs and biologics focus on specific named agents or even only a single agent, and identify characteristics associated with such focus.We evaluated 499 articles with meta-analyses published in 2010 and estimated how many did not cover all the available comparisons of tested interventions for a given condition (not all-inclusive); focused on specific named agent(s), or focused strictly on comparisons of only one specific active agent vs. placebo/no treatment or different doses/schedules.Of 499 eligible articles, 403 (80.8%) were not all-inclusive, 214 (42.9%) covered only specific named agent(s), and 74 (14.8%) examined only comparisons with one active agent vs. placebo/no treatment or different doses/schedules. Only 39 articles (7.8%) covered all possible indications for the examined agent(s). After adjusting for type of treatment/field, focus on specific named agent(s) was associated with publication in journal venues (odds ratio [OR]: 1.95; 95% confidence interval [CI]: 1.17-3.26) vs. Cochrane, industry sponsoring (OR: 3.94; 95% CI: 1.66-10.66), and individual patient data analyses (OR: 6.59; 95% CI: 2.24-19.39). Individual patient data analyses primarily (29/34) focused on specific named agent(s).The scope of meta-analysis publications frequently is narrow and shaped to serve particular agents.

Abstract

We explore some philosophical and scientific underpinnings of clinical research and evidence at the patient-clinician encounter scale. Insufficient evidence and a common failure to use replicable and sound research methods limit us. Both patients and health care may be, in part, complex nonlinear chaotic systems, and predicting their outcomes is a challenge. When trustworthy (credible) evidence is lacking, making correct clinical choices is often a low-probability exercise. Thus, human (clinician) error and consequent injury to patients appear inevitable. Individual clinician decision-makers operate under the philosophical influence of Adam Smith's "invisible hand" with resulting optimism that they will eventually make the right choices and cause health benefits. The presumption of an effective "invisible hand" operating in health-care delivery has supported a model in which individual clinicians struggle to practice medicine, as they see fit based on their own intuitions and preferences (and biases) despite the obvious complexity, errors, noise, and lack of evidence pervading the system. Not surprisingly, the "invisible hand" does not appear to produce the desired community health benefits. Obtaining a benefit at the patient-clinician encounter scale requires human (clinician) behavior modification. We believe that serious rethinking and restructuring of the clinical research and care delivery systems is necessary to assure the profession and the public that we continue to do more good than harm. We need to evaluate whether, and how, detailed decision-support tools may enable reproducible clinician behavior and beneficial use of evidence.

Abstract

Although observational studies provide useful descriptive and correlative information, their role in the evaluation of medical interventions remains contentious. There has been no systematic evaluation of authors' attitudes toward their own nonrandomized studies and how often they recommend specific medical practices.We reviewed all original articles of nonrandomized studies published in 2010 in New England Journal of Medicine, Lancet, Journal of the American Medical Association, and Annals of Internal Medicine. We classified articles based on whether authors recommend a medical practice and whether they state that a randomized trial is needed to support their recommendation. We also examined the types of logical extrapolations used by authors who did advance recommendations.Of the 631 original articles published in 2010, 298 (47%) articles were eligible observational studies. In 167 (56%) of 298 studies, authors recommended a medical practice based on their results. Only 24 (14%) of 167 studies stated that a randomized controlled trial (RCT) should be done to validate the recommendation, whereas the other 143 articles made a total of 149 logical extrapolations to recommend specific medical practices. Recommendations without a call for a randomized trial were most common in studies of modifiable factors (59%), but they were also common in studies reporting incidence or prevalence (51%), studies examining novel tests (41%), and association studies of nonmodifiable factors (32%).The authors of observational studies often extrapolate their results to make recommendations concerning a medical practice, typically without first calling for a RCT.

Abstract

Genetic association studies have revealed numerous polymorphisms conferring susceptibility to melanoma. We aimed to replicate previously discovered melanoma-associated single-nucleotide polymorphisms (SNPs) in a Greek case-control population, and examine their predictive value.Based on a field synopsis of genetic variants of melanoma (MelGene), we genotyped 284 patients and 284 controls at 34 melanoma-associated SNPs of which 19 derived from GWAS. We tested each one of the 33 SNPs passing quality control for association with melanoma both with and without accounting for the presence of well-established phenotypic risk factors. We compared the risk allele frequencies between the Greek population and the HapMap CEU sample. Finally, we evaluated the predictive ability of the replicated SNPs.Risk allele frequencies were significantly lower compared to the HapMap CEU for eight SNPs (rs16891982--SLC45A2, rs12203592--IRF4, rs258322--CDK10, rs1805007--MC1R, rs1805008--MC1R, rs910873--PIGU, rs17305573--PIGU, and rs1885120--MTAP) and higher for one SNP (rs6001027--PLA2G6) indicating a different profile of genetic susceptibility in the studied population. Previously identified effect estimates modestly correlated with those found in our population (r = 0.72, P<0.0001). The strongest associations were observed for rs401681-T in CLPTM1L (odds ratio [OR] 1.60, 95% CI 1.22-2.10; P = 0.001), rs16891982-C in SCL45A2 (OR 0.51, 95% CI 0.34-0.76; P = 0.001), and rs1805007-T in MC1R (OR 4.38, 95% CI 2.03-9.43; P = 2×10⁻⁵). Nominally statistically significant associations were seen also for another 5 variants (rs258322-T in CDK10, rs1805005-T in MC1R, rs1885120-C in MYH7B, rs2218220-T in MTAP and rs4911442-G in the ASIP region). The addition of all SNPs with nominal significance to a clinical non-genetic model did not substantially improve melanoma risk prediction (AUC for clinical model 83.3% versus 83.9%, p = 0.66).Overall, our study has validated genetic variants that are likely to contribute to melanoma susceptibility in the Greek population.

Abstract

Candidate genetic association studies have been found to have a low replication rate in the past. Here, we aimed to assess whether aspects of reported methodological characteristics in genetic association studies may be related to the magnitude of effects observed. An observational, literature-based investigation of 511 case-control studies of genetic association studies indexed in 2007, was undertaken. Meta-regression analyses were used to assess the relationship between 23 reported methodological characteristics and the magnitude of genetic associations. The 511 studies had been conducted in 52 countries and were published in 220 journals (median impact factor 5.1). The multivariate meta-regression model of methodological characteristics plus disease category accounted for 17.2 % of the between-study variance in the magnitude of the reported genetic associations. Our findings are consistent with the view that better conducted and better reported genetic association research may lead to less inflated results.

Abstract

Distinguishing true from false positive findings is a major challenge in human genetic epidemiology. Several strategies have been devised to facilitate this, including the positive predictive value (PPV) and a set of epidemiological criteria, known as the "Venice" criteria. The PPV measures the probability of a true association, given a statistically significant finding, while the Venice criteria grade the credibility based on the amount of evidence, consistency of replication and protection from bias. A vast majority of journals use significance thresholds to identify the true positive findings. We studied the effect of p value thresholds on the PPV and used the PPV and Venice criteria to define usable thresholds of statistical significance. Theoretical and empirical analyses of data published on AlzGene show that at a nominal p value threshold of 0.05 most "positive" findings will turn out to be false if the prior probability of association is below 0.10 even if the statistical power of the study is higher than 0.80. However, in underpowered studies (0.25) with a low prior probability of 1 × 10(-3), a p value of 1 × 10(-5) yields a high PPV (>96 %). Here we have shown that the p value threshold of 1 × 10(-5) gives a very strong evidence of association in almost all studies. However, in the case of a very high prior probability of association (0.50) a p value threshold of 0.05 may be sufficient, while for studies with very low prior probability of association (1 × 10(-4); genome-wide association studies for instance) 1 × 10(-7) may serve as a useful threshold to declare significance.

Abstract

Implantable cardioverter-defibrillators (ICDs) are recommended for the primary prevention of sudden cardiac death in patients with left ventricular dysfunction, but it is unclear whether treatment benefits are diminished in patients with very low baseline left ventricular ejection fraction (LVEF) (<25%) or increased in those with prolonged QRS duration (>120 ms).To study the effects of very low LVEF and prolonged QRS duration on the mortality benefits of ICD therapy.We performed a meta-analysis of primary prevention randomized controlled trials comparing ICD and standard medical therapy. All-cause mortality hazard ratios (HRs) in subgroups according to thresholds of 25% for LVEF and 120 ms for QRS duration were extracted from published reports or contributed by trial investigators and synthesized.There was no significant difference of ICD effectiveness in LVEF subgroups of 25%-35% (random effects HR 0.81; 95% confidence interval [CI] 0.70-0.94) vs<25% (HR 0.71; 95% CI 0.55-0.93). Results were also similar in the narrow and wide QRS subgroups (HR 0.78; 95% CI 0.68-0.90 and HR 0.70; 95% CI 0.51-0.95, respectively). Within the LVEF<25% and wide QRS subgroups, there was large heterogeneity driven by the Defibrillator in Acute Myocardial Infarction Trial that included patients with early post-myocardial infarction and its results (HR 1.49; 95% CI 0.84-2.68 and HR 1.51; 95% CI 0.83-2.83, respectively) differed significantly from other trials (P = .008 and P = .01, respectively).LVEF values and QRS duration do not appear to directly modify the survival benefit of ICD in patients with baseline LVEF<35%. However, patients with a recent myocardial infarction do not benefit from ICD, especially when they have LVEF<25% and/or wide QRS.

Abstract

Remarkable progress has been made in the last decade in new methods for biologic measurements using sophisticated technologies that go beyond the established genome, proteome, and gene expression platforms. These methods and technologies create opportunities to enhance cancer epidemiologic studies. In this article, we describe several emerging technologies and evaluate their potential in epidemiologic studies. We review the background, assays, methods, and challenges and offer examples of the use of mitochondrial DNA and copy number assessments, epigenomic profiling (including methylation, histone modification, miRNAs, and chromatin condensation), metabolite profiling (metabolomics), and telomere measurements. We map the volume of literature referring to each one of these measurement tools and the extent to which efforts have been made at knowledge integration (e.g., systematic reviews and meta-analyses). We also clarify strengths and weaknesses of the existing platforms and the range of type of samples that can be tested with each of them. These measurement tools can be used in identifying at-risk populations and providing novel markers of survival and treatment response. Rigorous analytic and validation standards, transparent availability of massive data, and integration in large-scale evidence are essential in fulfilling the potential of these technologies.

Abstract

Establishing the age of each mutation segregating in contemporary human populations is important to fully understand our evolutionary history and will help to facilitate the development of new approaches for disease-gene discovery. Large-scale surveys of human genetic variation have reported signatures of recent explosive population growth, notable for an excess of rare genetic variants, suggesting that many mutations arose recently. To more quantitatively assess the distribution of mutation ages, we resequenced 15,336 genes in 6,515 individuals of European American and African American ancestry and inferred the age of 1,146,401 autosomal single nucleotide variants (SNVs). We estimate that approximately 73% of all protein-coding SNVs and approximately 86% of SNVs predicted to be deleterious arose in the past 5,000-10,000?years. The average age of deleterious SNVs varied significantly across molecular pathways, and disease genes contained a significantly higher proportion of recently arisen deleterious SNVs than other genes. Furthermore, European Americans had an excess of deleterious variants in essential and Mendelian disease genes compared to African Americans, consistent with weaker purifying selection due to the Out-of-Africa dispersal. Our results better delimit the historical details of human protein-coding variation, show the profound effect of recent human history on the burden of deleterious SNVs segregating in contemporary populations, and provide important practical information that can be used to prioritize variants in disease-gene discovery.

Abstract

Meta-analyses are increasingly popular. It is unknown whether this popularity is driven by specific countries and specific meta-analyses types. PubMed was used to identify meta-analyses since 1995 (last update 9/1/2012) and catalogue their types and country of origin. We focused more on meta-analyses from China (the current top producer of meta-analyses) versus the USA (top producer until recently). The annual number of meta-analyses from China increased 40-fold between 2003 and 2011 versus 2.4-fold for the USA. The growth of Chinese meta-analyses was driven by genetics (110-fold increase in 2011 versus 2003). The HuGE Navigator identified 612 meta-analyses of genetic association studies published in 2012 from China versus only 109 from the USA. We compared in-depth 50 genetic association meta-analyses from China versus 50 from USA in 2012. Meta-analyses from China almost always used only literature-based data (92%), and focused on one or two genes (94%) and variants (78%) identified with candidate gene approaches (88%), while many USA meta-analyses used genome-wide approaches and raw data. Both groups usually concluded favorably for the presence of genetic associations (80% versus 74%), but nominal significance (P<0.05) typically sufficed in the China group. Meta-analyses from China typically neglected genome-wide data, and often included candidate gene studies published in Chinese-language journals. Overall, there is an impressive rise of meta-analyses from China, particularly on genetic associations. Since most claimed candidate gene associations are likely false-positives, there is an urgent global need to incorporate genome-wide data and state-of-the art statistical inferences to avoid a flood of false-positive genetic meta-analyses.

Abstract

Nutritional epidemiology is a highly prolific field. Debates on associations of nutrients with disease risk are common in the literature and attract attention in public media.We aimed to examine the conclusions, statistical significance, and reproducibility in the literature on associations between specific foods and cancer risk.We selected 50 common ingredients from random recipes in a cookbook. PubMed queries identified recent studies that evaluated the relation of each ingredient to cancer risk. Information regarding author conclusions and relevant effect estimates were extracted. When >10 articles were found, we focused on the 10 most recent articles.Forty ingredients (80%) had articles reporting on their cancer risk. Of 264 single-study assessments, 191 (72%) concluded that the tested food was associated with an increased (n = 103) or a decreased (n = 88) risk; 75% of the risk estimates had weak (0.05 > P ? 0.001) or no statistical (P > 0.05) significance. Statistically significant results were more likely than nonsignificant findings to be published in the study abstract than in only the full text (P < 0.0001). Meta-analyses (n = 36) presented more conservative results; only 13 (26%) reported an increased (n = 4) or a decreased (n = 9) risk (6 had more than weak statistical support). The median RRs (IQRs) for studies that concluded an increased or a decreased risk were 2.20 (1.60, 3.44) and 0.52 (0.39, 0.66), respectively. The RRs from the meta-analyses were on average null (median: 0.96; IQR: 0.85, 1.10).Associations with cancer risk or benefits have been claimed for most food ingredients. Many single studies highlight implausibly large effects, even though evidence is weak. Effect sizes shrink in meta-analyses.

Abstract

To assess how common it is to have multiple overlapping meta-analyses of randomized trials published on the same topic.Survey of published meta-analyses.PubMed.Meta-analyses published in 2010 were identified, and 5% of them were randomly selected. We further selected those that included randomized trials and examined effectiveness of any medical intervention. For eligible meta-analyses, we searched for other meta-analyses on the same topic (covering the same comparisons, indications/settings, and outcomes or overlapping subsets of them) published until February 2013.Of 73 eligible meta-analyses published in 2010, 49 (67%) had at least one other overlapping meta-analysis (median two meta-analyses per topic, interquartile range 1-4, maximum 13). In 17 topics at least one author was involved in at least two of the overlapping meta-analyses. No characteristics of the index meta-analyses were associated with the potential for overlapping meta-analyses. Among pairs of overlapping meta-analyses in 20 randomly selected topics, 13 of the more recent meta-analyses did not include any additional outcomes. In three of the four topics with eight or more published meta-analyses, many meta-analyses examined only a subset of the eligible interventions or indications/settings covered by the index meta-analysis. Conversely, for statins in the prevention of atrial fibrillation after cardiac surgery, 11 meta-analyses were published with similar eligibility criteria for interventions and setting: there was still variability on which studies were included, but the results were always similar or even identical across meta-analyses.While some independent replication of meta-analyses by different teams is possibly useful, the overall picture suggests that there is a waste of efforts with many topics covered by multiple overlapping meta-analyses.

Abstract

To compare treatment effects from randomised trials conducted in more developed versus less developed countries.Meta-epidemiological study.Cochrane Database of Systematic Reviews (August 2012).Meta-analyses with mortality outcomes including data from at least one randomised trial conducted in a less developed country and one in a more developed country. Relative risk estimates of more versus less developed countries were compared by calculating the relative relative risks for each topic and the summary relative relative risks across all topics. Similar analyses were performed for the primary binary outcome of each topic.139 meta-analyses with mortality outcomes were eligible. No nominally significant differences between more developed and less developed countries were found for 128 (92%) meta-analyses. However, differences were beyond chance in 11 (8%) cases, always showing more favourable treatment effects in trials from less developed countries. The summary relative relative risk was 1.12 (95% confidence interval 1.06 to 1.18; P<0.001; I(2)=0%), suggesting significantly more favourable mortality effects in trials from less developed countries. Results were similar for meta-analyses with nominally significant treatment effects for mortality (1.15), meta-analyses with recent trials (1.14), and when excluding trials from less developed countries that subsequently became more developed (1.12). For the primary binary outcomes (127 meta-analyses), 20 topics had differences in treatment effects beyond chance (more favourable in less developed countries in 15/20 cases).Trials from less developed countries in a few cases show significantly more favourable treatment effects than trials in more developed countries and, on average, treatment effects are more favourable in less developed countries. These discrepancies may reflect biases in reporting or study design as well as genuine differences in baseline risk or treatment implementation and should be considers when generalising evidence across different settings.

Abstract

Meta-analysis of multiple genome-wide association (GWA) studies has become common practice over the past few years. The main advantage of this technique is the maximization of power to detect subtle genetic effects for common traits. Moreover, one can use meta-analysis to probe and identify heterogeneity in the effect sizes across the combined studies. In this review, we systematically appraise and evaluate the characteristics of GWA meta-analyses with 10,000 or more subjects published up to June 2012. We provide an overview of the current landscape of variants discovered by GWA meta-analyses, and we discuss and assess with extrapolations from empirical data the value of larger meta-analyses for the discovery of additional genetic associations and new biology in the future. Finally, we discuss some emerging logistical and practical issues related to the conduct of meta-analysis of GWA studies. Expected final online publication date for the Annual Review of Genomics and Human Genetics Volume 14 is August 31, 2013. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.

Abstract

Knowledge integration includes knowledge management, synthesis, and translation processes. It aims to maximize the use of collected scientific information and accelerate translation of discoveries into individual and population health benefits. Accumulated evidence in cancer epidemiology constitutes a large share of the 2.7 million articles on cancer in PubMed. We examine the landscape of knowledge integration in cancer epidemiology. Past approaches have mostly used retrospective efforts of knowledge management and traditional systematic reviews and meta-analyses. Systematic searches identify 2,332 meta-analyses, about half of which are on genetics and epigenetics. Meta-analyses represent 1:89-1:1162 of published articles in various cancer subfields. Recently, there are more collaborative meta-analyses with individual-level data, including those with prospective collection of measurements [e.g., genotypes in genome-wide association studies (GWAS)]; this may help increase the reliability of inferences in the field. However, most meta-analyses are still done retrospectively with published information. There is also a flurry of candidate gene meta-analyses with spuriously prevalent "positive" results. Prospective design of large research agendas, registration of datasets, and public availability of data and analyses may improve our ability to identify knowledge gaps, maximize and accelerate translational progress or-at a minimum-recognize dead ends in a more timely fashion.

Abstract

Replication is essential for validating correct results, sorting out false-positive early discoveries, and improving the accuracy and precision of estimated effects. However, some types of seemingly successful replication may foster a spurious notion of increased credibility, if they are performed by the same team and propagate or extend the same errors made by the original discoveries. Besides same-team replication, replication by other teams may also succumb to inbreeding, if it cannot fiercely maintain its independence. These patterns include obedient replication and obliged replication. I discuss these replication patterns in the context of associations and effects in the psychological sciences, drawing from the criticism of Coyne and de Voogd of the proposed association between type D personality and cardiovascular mortality and other empirical examples.

Abstract

A comprehensive software for performing meta-analysis of ranked discovery oriented datasets, such as those derived from microarrays or other high throughput technologies, and for testing between-study heterogeneity for biological variables (gene expression, microRNA, proteomic, or other high-dimensional data) is presented. The software can identify biological probes that have either very high average ranks (e.g. consistently over-expressed genes) or very low average ranks (e.g. consistently under-expressed genes). The program tests each probe's average rank and the between-study heterogeneity of the study-specific ranks. Furthermore, it performs heterogeneity analyses restricted to probes with similar average ranks. The program allows both unweighted and weighted analysis. Statistical inferences are based on Monte Carlo permutation tests.

Abstract

Pre-eclampsia is thought to have a polygenic basis, but the identification of susceptibility genes and the quantification of associated risks have been elusive owing to lack of replication from published genetic association studies.To perform a systematic review and meta-analysis of genetic association studies to evaluate the evidence for the associations of various candidate genes with pre-eclampsia.For inclusion, studies had to involve unrelated subjects and examine the associations between pre-eclampsia (excluding publications without a measurement of proteinuria) and any candidate variant. Authors were contacted to obtain unpublished data when necessary. A meta-analysis was conducted for all variants with three or more independent samples available. Summary odds ratios (ORs), 99% confidence intervals (CIs) and P-values were calculated using random effects models.Data from 192 genetic association studies met the selection criteria and were included in 25 independent meta-analyses. There was some evidence of association for F5 rs6025 (OR = 1.74; 99% CI 1.43-2.12), F2 rs1799963 (OR = 1.72; 99% CI 1.31-2.26), ACE rs4646994 (OR = 1.17; 99% CI 0.99-1.40), AGT rs699 (OR = 1.26; 99% CI 1.00-1.59) and AGTR1 rs5186 (OR = 1.22; 99% CI 0.96-1.56), but only the first two associations reached moderate epidemiological credibility. Possible bias resulting from small study size and poor reporting of individual studies were the most important factors affecting the reported associations.To date, candidate gene studies in pre-eclampsia have not robustly documented any associations with strong epidemiological credibility. Large-scale replication of the most promising associations, exhibited by two genetic variants, and incorporation of agnostic high-throughput data may improve our genetic knowledge base for this complex phenotype.

Abstract

To examine whether perceived information gain (IG) drives the publication of randomized trials in high-impact factor (IF) journals.We estimated IG as the Kullback-Leibler divergence, quantifying how much a new finding changes established knowledge. We used 67 meta-analyses (964 randomized trials) that include one or more trials from any of the three highest IF general medical journals (NEJM, JAMA, and Lancet). We calculated IG for the presence of a non-null effect (IG(1)) and IG for the effect size magnitude (IG(2)).Across meta-analyses, the summary correlation coefficient of IF was 0.23 (95% confidence interval [CI]: 0.14, 0.31) for IG(1) and 0.35 (95% CI: 0.25, 0.46) for IG(2). IF also correlated with the P-value of the results (r=0.18), order of publication (r=-0.13), and number of events in the trial (r=0.36). Multivariate regression including IG, order of publication, P-value, and the number of events showed that IG is an independent correlate of IF. IG(2) explained a substantially larger proportion of the variance in IF than IG(1).Publication in journals with high IF is driven by how extensively the results of a study change prior perceptions of the evidence, independently of the statistical significance and size of the study.

Abstract

Many clinicians and decision makers want to know the combined effects of treatments that have not been evaluated in combination. It is possible to determine such treatment effects by making assumptions about the additive effects. We discuss here the prerequisites and methods of applying additivity assumptions in synthesizing the evidence from randomized trials and multiple treatment meta-analyses.Using statistical approaches, we demonstrate the utility of additivity of both pairwise randomized trials and multiple treatment comparison meta-analyses.We present illustratively an example on estimating the treatment effects of drug combinations for chronic obstructive pulmonary disease. We confirm the additive treatment effects by comparing with direct combination treatment trial results.Additive effects may be a useful tool to estimate the effectiveness of treatment combinations.

Abstract

Numerous biomarkers have been associated with cancer risk. We assessed whether there is evidence for excess statistical significance in results of cancer biomarker studies, suggesting biases.We systematically searched PubMed for meta-analyses of nongenetic biomarkers and cancer risk. The number of observed studies with statistically significant results was compared with the expected number, based on the statistical power of each study under different assumptions for the plausible effect size. We also evaluated small-study effects using asymmetry tests. All statistical tests were two-sided.We included 98 meta-analyses with 847 studies. Forty-three meta-analyses (44%) found nominally statistically significant summary effects (random effects). The proportion of meta-analyses with statistically significant effects was highest for infectious agents (86%), inflammatory (67%), and insulin-like growth factor (IGF)/insulin system (52%) biomarkers. Overall, 269 (32%) individual studies observed nominally statistically significant results. A statistically significant excess of the observed over the expected number of studies with statistically significant results was seen in 20 meta-analyses. An excess of observed vs expected was observed in studies of IGF/insulin (P ? .04) and inflammation systems (P ? .02). Only 12 meta-analyses (12%) had a statistically significant summary effect size, more than 1000 case patients, and no hints of small-study effects or excess statistical significance; only four of them had large effect sizes, three of which pertained to infectious agents (Helicobacter pylori, hepatitis and human papilloma viruses).Most well-documented biomarkers of cancer risk without evidence of bias pertain to infectious agents. Conversely, an excess of statistically significant findings was observed in studies of IGF/insulin and inflammation systems, suggesting reporting biases.

Abstract

A nutrient-wide approach may be useful to comprehensively test and validate associations between nutrients (derived from foods and supplements) and blood pressure (BP) in an unbiased manner.Data from 4680 participants aged 40 to 59 years in the cross-sectional International Study of Macro/Micronutrients and Blood Pressure (INTERMAP) were stratified randomly into training and testing sets. US National Health and Nutrition Examination Survey (NHANES) four cross-sectional cohorts (1999-2000, 2001-2002, 2003-2004, 2005-2006) were used for external validation. We performed multiple linear regression analyses associating each of 82 nutrients and 3 urine electrolytes with systolic and diastolic BP in the INTERMAP training set. Significant findings were validated in the INTERMAP testing set and further in the NHANES cohorts (false discovery rate <5% in training, P<0.05 for internal and external validation). Among the validated nutrients, alcohol and urinary sodium-to-potassium ratio were directly associated with systolic BP, and dietary phosphorus, magnesium, iron, thiamin, folacin, and riboflavin were inversely associated with systolic BP. In addition, dietary folacin and riboflavin were inversely associated with diastolic BP. The absolute effect sizes in the validation data (NHANES) ranged from 0.97 mm Hg lower systolic BP (phosphorus) to 0.39 mm Hg lower systolic BP (thiamin) per 1-SD difference in nutrient variable. Inclusion of nutrient intake from supplements in addition to foods gave similar results for some nutrients, though it attenuated the associations of folacin, thiamin, and riboflavin intake with BP.We identified significant inverse associations between B vitamins and BP, relationships hitherto poorly investigated. Our analyses represent a systematic unbiased approach to the evaluation and validation of nutrient-BP associations.

Abstract

To develop recommendations for the management of adult and paediatric lupus nephritis (LN).The available evidence was systematically reviewed using the PubMed database. A modified Delphi method was used to compile questions, elicit expert opinions and reach consensus.Immunosuppressive treatment should be guided by renal biopsy, and aiming for complete renal response (proteinuria <0.5 g/24 h with normal or near-normal renal function). Hydroxychloroquine is recommended for all patients with LN. Because of a more favourable efficacy/toxicity ratio, as initial treatment for patients with class III-IV(A) or (A/C) (±V) LN according to the International Society of Nephrology/Renal Pathology Society 2003 classification, mycophenolic acid (MPA) or low-dose intravenous cyclophosphamide (CY) in combination with glucocorticoids is recommended. In patients with adverse clinical or histological features, CY can be prescribed at higher doses, while azathioprine is an alternative for milder cases. For pure class V LN with nephrotic-range proteinuria, MPA in combination with oral glucocorticoids is recommended as initial treatment. In patients improving after initial treatment, subsequent immunosuppression with MPA or azathioprine is recommended for at least 3 years; in such cases, initial treatment with MPA should be followed by MPA. For MPA or CY failures, switching to the other agent, or to rituximab, is the suggested course of action. In anticipation of pregnancy, patients should be switched to appropriate medications without reducing the intensity of treatment. There is no evidence to suggest that management of LN should differ in children versus adults.Recommendations for the management of LN were developed using an evidence-based approach followed by expert consensus.

Abstract

Misclassification of phenotype status can seriously affect accuracy in association studies, including studies of genetic risk factors. A common problem is the classification of participants as nondiseased because of insufficient diagnostic workup or because participants have not been followed up long enough to develop disease. Some validated predictive models may have high discrimination in predicting disease. We suggest that information from such models can be used to predict the risk that a nondiseased participant will eventually develop disease and to recode the status of participants predicted to be at highest risk. We evaluate conditions under which recoding results in a maximal net improvement in the accuracy of phenotype classification. Net improvement is expected only when the positive likelihood ratio of the predictive model is larger than the inverse of the odds of disease among apparently nondiseased controls. We conducted simulations to probe the impact of reclassification on the power to detect new risk factors under several scenarios of classification accuracy of the previously developed models. We also apply this framework to a validated model of progression to advanced age-related macular degeneration that uses genetic and nongenetic variables (area under the curve = 0.915). In the training cohort (n = 2,937) and a separate validation cohort (n = 1,227), 195-272 and 78-91 nonprogressor participants, respectively, were reclassified as progressors. Correction of phenotype misclassification based on highly informative predictive models may be helpful in identifying additional genetic and other risk factors, when there are validated risk factors that provide strong discriminating ability.

Abstract

Two recent studies identified a mutation (p.Asp620Asn) in the vacuolar protein sorting 35 gene as a cause for an autosomal dominant form of Parkinson disease . Although additional missense variants were described, their pathogenic role yet remains inconclusive.We performed the largest multi-center study to ascertain the frequency and pathogenicity of the reported vacuolar protein sorting 35 gene variants in more than 15,000 individuals worldwide. p.Asp620Asn was detected in 5 familial and 2 sporadic PD cases and not in healthy controls, p.Leu774Met in 6 cases and 1 control, p.Gly51Ser in 3 cases and 2 controls. Overall analyses did not reveal any significant increased risk for p.Leu774Met and p.Gly51Ser in our cohort.Our study apart from identifying the p.Asp620Asn variant in familial cases also identified it in idiopathic Parkinson disease cases, and thus provides genetic evidence for a role of p.Asp620Asn in Parkinson disease in different populations worldwide.

Abstract

Most medical interventions have modest effects, but occasionally some clinical trials may find very large effects for benefits or harms.To evaluate the frequency and features of very large effects in medicine.Cochrane Database of Systematic Reviews (CDSR, 2010, issue 7).We separated all binary-outcome CDSR forest plots with comparisons of interventions according to whether the first published trial, a subsequent trial (not the first), or no trial had a nominally statistically significant (P < .05) very large effect (odds ratio [OR], ?5). We also sampled randomly 250 topics from each group for further in-depth evaluation.We assessed the types of treatments and outcomes in trials with very large effects, examined how often large-effect trials were followed up by other trials on the same topic, and how these effects compared against the effects of the respective meta-analyses.Among 85,002 forest plots (from 3082 reviews), 8239 (9.7%) had a significant very large effect in the first published trial, 5158 (6.1%) only after the first published trial, and 71,605 (84.2%) had no trials with significant very large effects. Nominally significant very large effects typically appeared in small trials with median number of events: 18 in first trials and 15 in subsequent trials. Topics with very large effects were less likely than other topics to address mortality (3.6% in first trials, 3.2% in subsequent trials, and 11.6% in no trials with significant very large effects) and were more likely to address laboratory-defined efficacy (10% in first trials,10.8% in subsequent, and 3.2% in no trials with significant very large effects). First trials with very large effects were as likely as trials with no very large effects to have subsequent published trials. Ninety percent and 98% of the very large effects observed in first and subsequently published trials, respectively, became smaller in meta-analyses that included other trials; the median odds ratio decreased from 11.88 to 4.20 for first trials, and from 10.02 to 2.60 for subsequent trials. For 46 of the 500 selected topics (9.2%; first and subsequent trials) with a very large-effect trial, the meta-analysis maintained very large effects with P < .001 when additional trials were included, but none pertained to mortality-related outcomes. Across the whole CDSR, there was only 1 intervention with large beneficial effects on mortality, P < .001, and no major concerns about the quality of the evidence (for a trial on extracorporeal oxygenation for severe respiratory failure in newborns).Most large treatment effects emerge from small studies, and when additional trials are performed, the effect sizes become typically much smaller. Well-validated large effects are uncommon and pertain to nonfatal outcomes.

Abstract

To assess the quantity and distribution of evidence from randomised controlled trials for the treatment of the major neglected tropical diseases and to identify gaps in the evidence with network analysis.Systematic review and network analysis.Cochrane Central Register of Controlled Trials and PubMed from inception to 31 August 2011.Randomised controlled trials that examined treatment of 16 neglected tropical diseases or complications thereof published in English, French, Spanish, Portuguese, German, or Dutch.We identified 971 eligible randomised trials. Leishmaniasis (184 trials, 23,039 participants) and geohelminth infections; 160 trials, 46,887 participants) were the most studied, while dracunculiasis (nine trials, 798 participants) and Buruli ulcer (five trials, 337 participants) were least studied. Relative to its global burden of disease, lymphatic filariasis had the fewest trials and participants. Only 11% of trials were industry funded. Either a single trial or trials with fewer than 100 participants comprised the randomised evidence for first or second line treatments for Buruli ulcer, human African trypanosomiasis, American trypanosomiasis, cysticercosis, rabies, echinococcosis, New World cutaneous leishmaniasis, and each of the foodborne trematode infections. Among the 10 disease categories with more than 40 trials, five lacked sufficient head to head comparisons between first or second line treatments.There is considerable variation in the amount of evidence from randomised controlled trials for each of the 16 major neglected tropical diseases. Even in diseases with substantial evidence, such as leishmaniasis and geohelminth infections, some recommended treatments have limited supporting data and lack head to head comparisons.

Abstract

Genetic differences between men and women may contribute to sex differences in prevalence and progression of many common complex diseases. Using the WTCCC GWAS, we analysed whether there are sex-specific differences in effect size estimates at 142 established loci for seven complex diseases: rheumatoid arthritis, type 1 diabetes (T1D), Crohn's disease, type 2 diabetes (T2D), hypertension, coronary artery disease and bipolar disorder.For each Single nucleotide polymorphism (SNP), we calculated the per-allele odds ratio for each sex and the relative odds ratios (RORs; the effect size is higher in men with ROR greater than one). RORs were then meta-analysed across loci within each disease and across diseases.For each disease, summary RORs were not different from one, but there was between-SNP heterogeneity in the RORs for T1D and T2D. Four loci in T1D, three in Crohn's disease and three in T2D showed differences in the genetic effect between men and women (P<0.05). We probed these differences in additional independent replication samples for T1D and T2D. The differences remained for the T1D loci CTSH, 17q21 and 20p13 and the T2D locus BCL11A, when WTCCC data and replication data were meta-analysed. Only CTSH showed different genetic effect between men and women in the replication data alone.Our results exclude the presence of large and frequent differences in the effect size estimates between men and women for the established loci in the seven common diseases explored. Documenting small differences in genetic effects between men and women requires large studies and systematic evaluation.

Abstract

Multiple treatment comparison (MTC) meta-analysis uses both direct (head-to-head) randomized clinical trial (RCT) evidence as well as indirect evidence from RCTs to compare the relative effectiveness of all included interventions. The methodological quality of MTCs may be difficult for clinicians to interpret because the number of interventions evaluated may be large and the methodological approaches may be complex. Clinicians and others evaluating an MTC should be aware of the potential biases that can affect the interpretation of these analyses. Readers should consider whether the primary studies are sufficiently homogeneous to combine; whether the different interventions are sufficiently similar in their populations, study designs, and outcomes; and whether the direct evidence is sufficiently similar to the indirect evidence to consider combining. This article uses the existing Users' Guides format to address study validity, interpretation of results, and application to a patient scenario.

Abstract

Published evidence suggests that aspects of trial design lead to biased intervention effect estimates, but findings from different studies are inconsistent. This study combined data from 7 meta-epidemiologic studies and removed overlaps to derive a final data set of 234 unique meta-analyses containing 1973 trials. Outcome measures were classified as "mortality," "other objective," "or subjective," and Bayesian hierarchical models were used to estimate associations of trial characteristics with average bias and between-trial heterogeneity. Intervention effect estimates seemed to be exaggerated in trials with inadequate or unclear (vs. adequate) random-sequence generation (ratio of odds ratios, 0.89 [95% credible interval {CrI}, 0.82 to 0.96]) and with inadequate or unclear (vs. adequate) allocation concealment (ratio of odds ratios, 0.93 [CrI, 0.87 to 0.99]). Lack of or unclear double-blinding (vs. double-blinding) was associated with an average of 13% exaggeration of intervention effects (ratio of odds ratios, 0.87 [CrI, 0.79 to 0.96]), and between-trial heterogeneity was increased for such studies (SD increase in heterogeneity, 0.14 [CrI, 0.02 to 0.30]). For each characteristic, average bias and increases in between-trial heterogeneity were driven primarily by trials with subjective outcomes, with little evidence of bias in trials with objective and mortality outcomes. This study is limited by incomplete trial reporting, and findings may be confounded by other study design characteristics. Bias associated with study design characteristics may lead to exaggeration of intervention effect estimates and increases in between-trial heterogeneity in trials reporting subjectively assessed outcomes.

Abstract

Because of a variety of caveats, the safety and effectiveness of interventions in human subjects can only be speculated from animal studies. Careful synthesis of data from multiple animal studies is needed to begin to assess the likelihood of successful cross-species translation (Fay et al., this issue).

Abstract

While large numbers of proteomic biomarkers have been described, they are generally not implemented in medical practice. We have investigated the reasons for this shortcoming, focusing on hurdles downstream of biomarker verification, and describe major obstacles and possible solutions to ease valid biomarker implementation. Some of the problems lie in suboptimal biomarker discovery and validation, especially lack of validated platforms with well-described performance characteristics to support biomarker qualification. These issues have been acknowledged and are being addressed, raising the hope that valid biomarkers may start accumulating in the foreseeable future. However, successful biomarker discovery and qualification alone does not suffice for successful implementation. Additional challenges include, among others, limited access to appropriate specimens and insufficient funding, the need to validate new biomarker utility in interventional trials, and large communication gaps between the parties involved in implementation. To address this problem, we propose an implementation roadmap. The implementation effort needs to involve a wide variety of stakeholders (clinicians, statisticians, health economists, and representatives of patient groups, health insurance, pharmaceutical companies, biobanks, and regulatory agencies). Knowledgeable panels with adequate representation of all these stakeholders may facilitate biomarker evaluation and guide implementation for the specific context of use. This approach may avoid unwarranted delays or failure to implement potentially useful biomarkers, and may expedite meaningful contributions of the biomarker community to healthcare.

Abstract

A recent genome-wide association study in patients with panic disorder (PD) identified a risk haplotype consisting of two single-nucleotide polymorphisms (SNPs) (rs7309727 and rs11060369) located in intron 3 of TMEM132D to be associated with PD in three independent samples. Now we report a subsequent confirmation study using five additional PD case-control samples (n = 1670 cases and n = 2266 controls) assembled as part of the Panic Disorder International Consortium (PanIC) study for a total of 2678 cases and 3262 controls in the analysis. In the new independent samples of European ancestry (EA), the association of rs7309727 and the risk haplotype rs7309727-rs11060369 was, indeed, replicated, with the strongest signal coming from patients with primary PD, that is, patients without major psychiatric comorbidities (n = 1038 cases and n = 2411 controls). This finding was paralleled by the results of the meta-analysis across all samples, in which the risk haplotype and rs7309727 reached P-levels of P = 1.4e-8 and P = 1.1e-8, respectively, when restricting the samples to individuals of EA with primary PD. In the Japanese sample no associations with PD could be found. The present results support the initial finding that TMEM132D gene contributes to genetic susceptibility for PD in individuals of EA. Our results also indicate that patient ascertainment and genetic background could be important sources of heterogeneity modifying this association signal in different populations.

Abstract

Osteoarthritis is the most common form of arthritis worldwide and is a major cause of pain and disability in elderly people. The health economic burden of osteoarthritis is increasing commensurate with obesity prevalence and longevity. Osteoarthritis has a strong genetic component but the success of previous genetic studies has been restricted due to insufficient sample sizes and phenotype heterogeneity.We undertook a large genome-wide association study (GWAS) in 7410 unrelated and retrospectively and prospectively selected patients with severe osteoarthritis in the arcOGEN study, 80% of whom had undergone total joint replacement, and 11,009 unrelated controls from the UK. We replicated the most promising signals in an independent set of up to 7473 cases and 42,938 controls, from studies in Iceland, Estonia, the Netherlands, and the UK. All patients and controls were of European descent.We identified five genome-wide significant loci (binomial test p?5·0×10(-8)) for association with osteoarthritis and three loci just below this threshold. The strongest association was on chromosome 3 with rs6976 (odds ratio 1·12 [95% CI 1·08-1·16]; p=7·24×10(-11)), which is in perfect linkage disequilibrium with rs11177. This SNP encodes a missense polymorphism within the nucleostemin-encoding gene GNL3. Levels of nucleostemin were raised in chondrocytes from patients with osteoarthritis in functional studies. Other significant loci were on chromosome 9 close to ASTN2, chromosome 6 between FILIP1 and SENP6, chromosome 12 close to KLHDC5 and PTHLH, and in another region of chromosome 12 close to CHST11. One of the signals close to genome-wide significance was within the FTO gene, which is involved in regulation of bodyweight-a strong risk factor for osteoarthritis. All risk variants were common in frequency and exerted small effects.Our findings provide insight into the genetics of arthritis and identify new pathways that might be amenable to future therapeutic intervention.arcOGEN was funded by a special purpose grant from Arthritis Research UK.

Abstract

The design of randomised controlled trials (RCTs) should incorporate characteristics (such as concealment of randomised allocation and blinding of participants and personnel) that avoid biases resulting from lack of comparability of the intervention and control groups. Empirical evidence suggests that the absence of such characteristics leads to biased intervention effect estimates, but the findings of different studies are not consistent.To examine the influence of unclear or inadequate random sequence generation and allocation concealment, and unclear or absent double blinding, on intervention effect estimates and between-trial heterogeneity, and whether or not these influences vary with type of clinical area, intervention, comparison and outcome measure.Data were combined from seven contributing meta-epidemiological studies (collections of meta-analyses in which trial characteristics are assessed and results recorded). The resulting database was used to identify and remove overlapping meta-analyses. Outcomes were coded such that odds ratios < 1 correspond to beneficial intervention effects. Outcome measures were classified as mortality, other objective or subjective. We examined agreement between assessments of trial characteristics in trials assessed in more than one contributing study. We used hierarchical Bayesian bias models to estimate the effect of trial characteristics on average bias [quantified as ratios of odds ratios (RORs) with 95% credible intervals (CrIs) comparing trials with and without a characteristic] and in increasing between-trial heterogeneity.The analysis data set contained 1973 trials included in 234 meta-analyses. Median kappa statistics for agreement between assessments of trial characteristics were: sequence generation 0.60, allocation concealment 0.58 and blinding 0.87. Intervention effect estimates were exaggerated by an average 11% in trials with inadequate or unclear (compared with adequate) sequence generation (ROR 0.89, 95% CrI 0.82 to 0.96); between-trial heterogeneity was higher among such trials. Bias associated with inadequate or unclear sequence generation was greatest for subjective outcomes (ROR 0.83, 95% CrI 0.74 to 0.94) and the increase in heterogeneity was greatest for such outcomes [standard deviation (SD) 0.20, 95% CrI 0.03 to 0.32]. The effect of inadequate or unclear (compared with adequate) allocation concealment was greatest among meta-analyses with a subjectively assessed outcome intervention effect (ROR 0.85, 95% CrI 0.75 to 0.95), and the increase in between-trial heterogeneity was also greatest for such outcomes (SD 0.20, 95% CrI 0.02 to 0.33). Lack of, or unclear, double blinding (compared with double blinding) was associated with an average 13% exaggeration of intervention effects (ROR 0.87, 95% CrI 0.79 to 0.96), and between-trial heterogeneity was increased for such studies (SD 0.14, 95% CrI 0.02 to 0.30). Average bias (ROR 0.78, 95% CrI 0.65 to 0.92) and between-trial heterogeneity (SD 0.37, 95% CrI 0.19 to 0.53) were greatest for meta-analyses assessing subjective outcomes. Among meta-analyses with subjectively assessed outcomes, the effect of lack of blinding appeared greater than the effect of inadequate or unclear sequence generation or allocation concealment.Bias associated with specific reported study design characteristics leads to exaggeration of beneficial intervention effect estimates and increases in between-trial heterogeneity. For each of the three characteristics assessed, these effects were greatest for subjectively assessed outcomes. Assessments of the risk of bias in RCTs should account for these findings. Further research is needed to understand the effects of attrition bias, as well as the relative importance of blinding of patients, care-givers and outcome assessors, and thus separate the effects of performance and detection bias.National Institute for Health Research Health Technology Assessment programme.

Abstract

To investigate whether the 2 subtypes of advanced age-related macular degeneration (AMD), choroidal neovascularization (CNV), and geographic atrophy (GA) segregate separately in families and to identify which genetic variants are associated with these 2 subtypes.Sibling correlation study and genome-wide association study (GWAS).For the sibling correlation study, 209 sibling pairs with advanced AMD were included. For the GWAS, 2594 participants with advanced AMD subtypes and 4134 controls were included. Replication cohorts included 5383 advanced AMD participants and 15 240 controls.Participants had the AMD grade assigned based on fundus photography, examination, or both. To determine heritability of advanced AMD subtypes, a sibling correlation study was performed. For the GWAS, genome-wide genotyping was conducted and 6 036 699 single nucleotide polymorphisms (SNPs) were imputed. Then, the SNPs were analyzed with a generalized linear model controlling for genotyping platform and genetic ancestry. The most significant associations were evaluated in independent cohorts.Concordance of advanced AMD subtypes in sibling pairs and associations between SNPs with GA and CNV advanced AMD subtypes.The difference between the observed and expected proportion of siblings concordant for the same subtype of advanced AMD was different to a statistically significant degree (P = 4.2 × 10(-5)), meaning that in siblings of probands with CNV or GA, the same advanced subtype is more likely to develop. In the analysis comparing participants with CNV to those with GA, a statistically significant association was observed at the ARMS2/HTRA1 locus (rs10490924; odds ratio [OR], 1.47; P = 4.3 × 10(-9)), which was confirmed in the replication samples (OR, 1.38; P = 7.4 × 10(-14) for combined discovery and replication analysis).Whether CNV versus GA develops in a patient with AMD is determined in part by genetic variation. In this large GWAS meta-analysis and replication analysis, the ARMS2/HTRA1 locus confers increased risk for both advanced AMD subtypes, but imparts greater risk for CNV than for GA. This locus explains a small proportion of the excess sibling correlation for advanced AMD subtype. Other loci were detected with suggestive associations that differ for advanced AMD subtypes and deserve follow-up in additional studies.

Abstract

Advances in laboratory techniques have led to a rapidly increasing use of biomarkers in epidemiological studies. Biomarkers of internal dose, early biological change, susceptibility, and clinical outcomes are used as proxies for investigating the interactions between external and/or endogenous agents and the body components or processes. The need for improved reporting of scientific research led to influential statements of recommendations such as STrengthening Reporting of Observational studies in Epidemiology (STROBE) statement. The STROBE initiative established in 2004 aimed to provide guidance on how to report observational research. Its guidelines provide a user-friendly checklist of 22 items to be reported in epidemiological studies, with items specific to the three main study designs: cohort studies, case-control studies and cross-sectional studies. The present STrengthening the Reporting of OBservational studies in Epidemiology - Molecular Epidemiology (STROBE-ME) initiative builds on the STROBE Statement implementing 9 existing items of STROBE and providing 17 additional items to the 22 items of STROBE checklist. The additions relate to the use of biomarkers in epidemiological studies, concerning collection, handling and storage of biological samples; laboratory methods, validity and reliability of biomarkers; specificities of study design; and ethical considerations. The STROBE-ME recommendations are intended to complement the STROBE recommendations.

Abstract

A predisposition for thoracic aortic aneurysms leading to acute aortic dissections can be inherited in families in an autosomal dominant manner. Genome-wide linkage analysis of two large unrelated families with thoracic aortic disease followed by whole-exome sequencing of affected relatives identified causative mutations in TGFB2. These mutations-a frameshift mutation in exon 6 and a nonsense mutation in exon 4-segregated with disease with a combined logarithm of odds (LOD) score of 7.7. Sanger sequencing of 276 probands from families with inherited thoracic aortic disease identified 2 additional TGFB2 mutations. TGFB2 encodes transforming growth factor (TGF)-?2, and the mutations are predicted to cause haploinsufficiency for TGFB2; however, aortic tissue from cases paradoxically shows increased TGF-?2 expression and immunostaining. Thus, haploinsufficiency for TGFB2 predisposes to thoracic aortic disease, suggesting that the initial pathway driving disease is decreased cellular TGF-?2 levels leading to a secondary increase in TGF-?2 production in the diseased aorta.

Abstract

Eleven genetic loci have reached genome-wide significance in a recent meta-analysis of genome-wide association studies in Parkinson disease (PD) based on populations of Caucasian descent. The extent to which these genetic effects are consistent across different populations is unknown.Investigators from the Genetic Epidemiology of Parkinson's Disease Consortium were invited to participate in the study. A total of 11 SNPs were genotyped in 8,750 cases and 8,955 controls. Fixed as well as random effects models were used to provide the summary risk estimates for these variants. We evaluated between-study heterogeneity and heterogeneity between populations of different ancestry.In the overall analysis, single nucleotide polymorphisms (SNPs) in 9 loci showed significant associations with protective per-allele odds ratios of 0.78-0.87 (LAMP3, BST1, and MAPT) and susceptibility per-allele odds ratios of 1.14-1.43 (STK39, GAK, SNCA, LRRK2, SYT11, and HIP1R). For 5 of the 9 replicated SNPs there was nominally significant between-site heterogeneity in the effect sizes (I(2) estimates ranged from 39% to 48%). Subgroup analysis by ethnicity showed significantly stronger effects for the BST1 (rs11724635) in Asian vs Caucasian populations and similar effects for SNCA, LRRK2, LAMP3, HIP1R, and STK39 in Asian and Caucasian populations, while MAPT rs2942168 and SYT11 rs34372695 were monomorphic in the Asian population, highlighting the role of population-specific heterogeneity in PD.Our study allows insight to understand the distribution of newly identified genetic factors contributing to PD and shows that large-scale evaluation in diverse populations is important to understand the role of population-specific heterogeneity.

Abstract

Exome sequencing has become a powerful and effective strategy for the discovery of genes underlying Mendelian disorders. However, use of exome sequencing to identify variants associated with complex traits has been more challenging, partly because the sample sizes needed for adequate power may be very large. One strategy to increase efficiency is to sequence individuals who are at both ends of a phenotype distribution (those with extreme phenotypes). Because the frequency of alleles that contribute to the trait are enriched in one or both phenotype extremes, a modest sample size can potentially be used to identify novel candidate genes and/or alleles. As part of the National Heart, Lung, and Blood Institute (NHLBI) Exome Sequencing Project (ESP), we used an extreme phenotype study design to discover that variants in DCTN4, encoding a dynactin protein, are associated with time to first P. aeruginosa airway infection, chronic P. aeruginosa infection and mucoid P. aeruginosa in individuals with cystic fibrosis.

Abstract

Optimal treatment decisions in children require sufficient evidence on the safety and efficacy of pharmaceuticals in pediatric patients. However, there is concern that not enough trials are conducted in children and that pediatric trials differ from those performed in adults. Our objective was to measure the prevalence of pediatric studies among clinical drug trials and compare trial characteristics and quality indicators between pediatric and adult drug trials.For conditions representing a high burden of pediatric disease, we identified all drug trials registered in ClinicalTrials.gov with start dates between 2006 and 2011 and tracked the resulting publications. We measured the proportion of pediatric trials and subjects for each condition and compared pediatric and adult trial characteristics and quality indicators.For the conditions selected, 59.9% of the disease burden was attributable to children, but only 12.0% (292/2440) of trials were pediatric (P < .001). Among pediatric trials, 58.6% were conducted without industry funding compared with 35.0% of adult trials (P < .001). Fewer pediatric compared with adult randomized trials examined safety outcomes (10.1% vs 16.9%, P = .008). Pediatric randomized trials were slightly more likely to be appropriately registered before study start (46.9% vs 39.3%, P = .04) and had a modestly higher probability of publication in the examined time frame (32.8% vs 23.2%, P = .04).There is substantial discrepancy between pediatric burden of disease and the amount of clinical trial research devoted to pediatric populations. This may be related in part to trial funding, with pediatric trials relying primarily on government and nonprofit organizations.

Abstract

Genome-wide association studies have identified multiple genetic susceptibility variants to several complex human diseases. However, risk-genotype frequency at loci showing robust associations might differ substantially among different populations. In this paper, we present methods to assess the contribution of genetic variants to the difference in the incidence of disease between different population groups for different scenarios. We derive expressions for the contribution of a single genetic variant, multiple genetic variants, and the contribution of the joint effect of a genetic variant and an environmental factor to the difference in the incidence of disease. The contribution of genetic variants to the difference in incidence increases with increasing difference in risk-genotype frequency, but declines with increasing difference in incidence between the two populations. The contribution of genetic variants also increases with increasing relative risk and the contribution of joint effect of genetic and environmental factors increases with increasing relative risk of the gene-environmental interaction. The contribution of genetic variants to the difference in incidence between two populations can be expressed as a function of the population attributable risks of the genetic variants in the two populations. The contribution of a group of genetic variants to the disparity in incidence of disease could change considerably by adding one more genetic variant to the group. Any estimate of genetic contribution to the disparity in incidence of disease between two populations at this stage seems to be an elusive goal.

Abstract

Pain influences sleep and vice versa. We performed an umbrella review of meta-analyses on treatments for diverse conditions in order to examine whether diverse medical treatments for different conditions have similar or divergent effects on pain and sleep.We searched published systematic reviews with meta-analyses in the Cochrane Database of Systematic Reviews until October 20, 2011. We identified randomized trials (or meta-analyses thereof, when >1 trial was available) where both pain and sleep outcomes were examined. Pain outcomes were categorized as headache, musculoskeletal, abdominal, pelvic, generic or other pain. Sleep outcomes included insomnia, sleep disruption, and sleep disturbance. We estimated odds ratios for all outcomes and evaluated the concordance in the direction of effects between sleep and various types of pain and the correlation of treatment effects between sleep and pain outcomes.151 comparisons with 385 different trials met our eligibility criteria. 96 comparisons had concordant direction of effects between each pain outcome and sleep, while in 55 the effect estimates were in opposite directions (P<0.0001). In the 20 comparisons with largest amount of evidence, the experimental drug always had worse sleep outcomes and tended to have worse pain outcomes in 17/20 cases. For headache and musculoskeletal pain, 69 comparisons showed concordant direction of effects with sleep outcomes and 36 showed discordant direction (P<0.0001). For the other 4 pain types there were overall 27 vs. 19 pairs with concordant vs. discordant direction of effects (P?=?0.095). There was a weak correlation of the treatment effect sizes for sleep vs. headache/musculoskeletal pain (r?=?0.17, P?=?0.092).Medical interventions tend to have effects in the same direction for pain and sleep outcomes, but exceptions occur. Concordance is primarily seen for sleep and headache or musculoskeletal pain where many drugs may both disturb sleep and cause pain.

Abstract

It is not well known whether genetic markers identified through genome-wide association studies (GWAS) confer similar or different risks across people of different ancestry. We screened a regularly updated catalog of all published GWAS curated at the NHGRI website for GWAS-identified associations that had reached genome-wide significance (p ? 5 × 10(-8)) in at least one major ancestry group (European, Asian, African) and for which replication data were available for comparison in at least two different major ancestry groups. These groups were compared for the correlation between and differences in risk allele frequencies and genetic effects' estimates. Data on 108 eligible GWAS-identified associations with a total of 900 datasets (European, n = 624; Asian, n = 217; African, n = 60) were analyzed. Risk-allele frequencies were modestly correlated between ancestry groups, with >10% absolute differences in 75-89% of the three pairwise comparisons of ancestry groups. Genetic effect (odds ratio) point estimates between ancestry groups correlated modestly (pairwise comparisons' correlation coefficients: 0.20-0.33) and point estimates of risks were opposite in direction or differed more than twofold in 57%, 79%, and 89% of the European versus Asian, European versus African, and Asian versus African comparisons, respectively. The modest correlations, differing risk estimates, and considerable between-association heterogeneity suggest that differential ancestral effects can be anticipated and genomic risk markers may need separate further evaluation in different ancestry groups.

Abstract

Fifteen meta-analyses have been published between 1995 and 2011 to evaluate the efficacy/effectiveness and harms of diverse influenza vaccines--seasonal, H5N1 and 2009 (H1N1)--in various age-classes (healthy children, adults or elderly). These meta-analyses have often adopted different analyses and study selection criteria. Because it is difficult to have a clear picture of vaccine benefits and harms examining single systematic reviews, we compiled the main findings and evaluated which could be the most reasonable explanations for some differences in findings (or their interpretation) across previously published meta-analyses. For each age group, we performed analyses that included all trials that had been included in at least one relevant meta-analysis, also exploring whether effect sizes changed over time. Although we identified several discrepancies among the meta-analyses on seasonal vaccines for children and elderly, overall most seasonal influenza vaccines showed statistically significant efficacy/effectiveness, which was acceptable or high for laboratory-confirmed cases and of modest magnitude for clinically-confirmed cases. The available evidence on parenteral inactivated vaccines for children aged < 2 y remains scarce. Pre-pandemic "avian" H5N1 and pandemic 2009 (H1N1) vaccines can achieve satisfactory immunogenicity, but no meta-analysis has addressed H1N1 vaccination impact on clinical outcomes. Data on harms are overall reassuring, but their value is diminished by inconsistent reporting.

Abstract

To assess the interpretation of a highly heterogeneous meta-analysis by authors of primary studies and by methodologists.We surveyed the authors of studies on the association between insulin-like growth factor 1 (IGF-1) and prostate cancer, and 20 meta-analysis methodologists. Authors and methodologists presented with the respective meta-analysis results were queried about the effect size and potential causality of the association. We evaluated whether author responses correlated with the number of IGF-related articles they had published and their study results included in the meta-analysis. We also compared authors' and methodologists' responses.Authors who had published more IGF-related papers offered more generous effect size estimates for the association (?(s)=0.61, P=0.01) and higher likelihood that the odds ratio (OR) was greater than 1.20 (?(s)=0.63, P=0.01). Authors who had published themselves studies with statistically significant effects for a positive association were more likely to believe that the true OR is greater than 1.20 compared with methodologists (median likelihood 50% versus 2.5%, P=0.01).Researchers are influenced by their own investment in the field, when interpreting a meta-analysis that includes their own study. Authors who published significant results are more likely to believe that a strong association exists compared with methodologists.

Abstract

Both genetic and environmental factors contribute to triglyceride, low-density lipoprotein-cholesterol (LDL-C), and high-density lipoprotein-cholesterol (HDL-C) levels. Although genome-wide association studies are currently testing the genetic factors systematically, testing and reporting one or a few factors at a time can lead to fragmented literature for environmental chemical factors. We screened for correlation between environmental factors and lipid levels, utilizing four independent surveys with information on 188 environmental factors from the Centers of Disease Control, National Health and Nutrition Examination Survey, collected between 1999 and 2006.We used linear regression to correlate each environmental chemical factor to triglycerides, LDL-C and HDL-C adjusting for age, age(2), sex, ethnicity, socio-economic status and body mass index. Final estimates were adjusted for waist circumference, diabetes status, blood pressure and survey. Multiple comparisons were controlled for by estimating the false discovery rate and significant findings were tentatively validated in an independent survey.We identified and validated 29, 9 and 17 environmental factors correlated with triglycerides, LDL-C and HDL-C levels, respectively. Findings include hydrocarbons and nicotine associated with lower HDL-C and vitamin E (?-tocopherol) associated with unfavourable lipid levels. Higher triglycerides and lower HDL-C were correlated with higher levels of fat-soluble contaminants (e.g. polychlorinated biphenyls and dibenzofurans). Nutrients and vitamin markers (e.g. vitamins B, D and carotenes), were associated with favourable triglyceride and HDL-C levels.Our systematic association study has enabled us to postulate about broad environmental correlation to lipid levels. Although subject to confounding and reverse causality bias, these findings merit evaluation in additional cohorts.

Abstract

We propose guidelines to evaluate the cumulative evidence of gene-environment (G?×?E) interactions in the causation of human cancer. Our approach has its roots in the HuGENet and IARC Monographs evaluation processes for genetic and environmental risk factors, respectively, and can be applied to common chronic diseases other than cancer. We first review issues of definitions of G?×?E interactions, discovery and modelling methods for G?×?E interactions, and issues in systematic reviews of evidence for G?×?E interactions, since these form the foundation for appraising the credibility of evidence in this contentious field. We then propose guidelines that include four steps: (i) score the strength of the evidence for main effects of the (a) environmental exposure and (b) genetic variant; (ii) establish a prior score category and decide on the pattern of interaction to be expected; (iii) score the strength of the evidence for interaction between the environmental exposure and the genetic variant; and (iv) examine the overall plausibility of interaction by combining the prior score and the strength of the evidence and interpret results. We finally apply the scheme to the interaction between NAT2 polymorphism and tobacco smoking in determining bladder cancer risk.

Abstract

An important step toward improvement of the conduct of pediatric clinical research is the standardization of the ages of children to be included in pediatric trials and the optimal age-subgroups to be analyzed.We set out to evaluate empirically the age ranges of children, and age-subgroup analyses thereof, reported in recent pediatric randomized clinical trials (RCTs) and meta-analyses. First, we screened 24 RCTs published in Pediatrics during the first 6 months of 2011; second, we screened 188 pediatric RCTs published in 2007 in the Cochrane Central Register of Controlled Trials; third, we screened 48 pediatric meta-analyses published in the Cochrane Database of Systematic Reviews in 2011. We extracted information on age ranges and age-subgroups considered and age-subgroup differences reported.The age range of children in RCTs published in Pediatrics varied from 0.1 to 17.5 years (median age: 5; interquartile range: 1.8-10.2) and only 25% of those presented age-subgroup analyses. Large variability was also detected for age ranges in 188 RCTs from the Cochrane Central Register of Controlled Trials, and only 28 of those analyzed age-subgroups. Moreover, only 11 of 48 meta-analyses had age-subgroup analyses, and in 6 of those, only different studies were included. Furthermore, most of these observed differences were not beyond chance.We observed large variability in the age ranges and age-subgroups of children included in recent pediatric trials and meta-analyses. Despite the limited available data, some age-subgroup differences were noted. The rationale for the selection of particular age-subgroups deserves further study.

Abstract

To evaluate the evidence on comparisons of established cardiovascular risk prediction models and to collect comparative information on their relative prognostic performance.Systematic review of comparative predictive model studies.Medline and screening of citations and references.Studies examining the relative prognostic performance of at least two major risk models for cardiovascular disease in general populations.Information on study design, assessed risk models, and outcomes. We examined the relative performance of the models (discrimination, calibration, and reclassification) and the potential for outcome selection and optimism biases favouring newly introduced models and models developed by the authors.20 articles including 56 pairwise comparisons of eight models (two variants of the Framingham risk score, the assessing cardiovascular risk to Scottish Intercollegiate Guidelines Network to assign preventative treatment (ASSIGN) score, systematic coronary risk evaluation (SCORE) score, Prospective Cardiovascular Münster (PROCAM) score, QRESEARCH cardiovascular risk (QRISK1 and QRISK2) algorithms, Reynolds risk score) were eligible. Only 10 of 56 comparisons exceeded a 5% relative difference based on the area under the receiver operating characteristic curve. Use of other discrimination, calibration, and reclassification statistics was less consistent. In 32 comparisons, an outcome was used that had been used in the original development of only one of the compared models, and in 25 of these comparisons (78%) the outcome-congruent model had a better area under the receiver operating characteristic curve. Moreover, authors always reported better area under the receiver operating characteristic curves for models that they themselves developed (in five articles on newly introduced models and in three articles on subsequent evaluations).Several risk prediction models for cardiovascular disease are available and their head to head comparisons would benefit from standardised reporting and formal, consistent statistical comparisons. Outcome selection and optimism biases apparently affect this literature.

Abstract

Bone mineral density (BMD) is the most widely used predictor of fracture risk. We performed the largest meta-analysis to date on lumbar spine and femoral neck BMD, including 17 genome-wide association studies and 32,961 individuals of European and east Asian ancestry. We tested the top BMD-associated markers for replication in 50,933 independent subjects and for association with risk of low-trauma fracture in 31,016 individuals with a history of fracture (cases) and 102,444 controls. We identified 56 loci (32 new) associated with BMD at genome-wide significance (P < 5 × 10(-8)). Several of these factors cluster within the RANK-RANKL-OPG, mesenchymal stem cell differentiation, endochondral ossification and Wnt signaling pathways. However, we also discovered loci that were localized to genes not known to have a role in bone biology. Fourteen BMD-associated loci were also associated with fracture risk (P < 5 × 10(-4), Bonferroni corrected), of which six reached P < 5 × 10(-8), including at 18p11.21 (FAM210A), 7q21.3 (SLC25A13), 11q13.2 (LRP5), 4q22.1 (MEPE), 2p16.2 (SPTBN1) and 10q21.1 (DKK1). These findings shed light on the genetic architecture and pathophysiological mechanisms underlying BMD variation and fracture susceptibility.

Abstract

Rigorous methodological standards help to ensure that medical research produces information that is valid and generalizable, and are essential in patient-centered outcomes research (PCOR). Patient-centeredness refers to the extent to which the preferences, decision-making needs, and characteristics of patients are addressed, and is the key characteristic differentiating PCOR from comparative effectiveness research. The Patient Protection and Affordable Care Act signed into law in 2010 created the Patient-Centered Outcomes Research Institute (PCORI), which includes an independent, federally appointed Methodology Committee. The Methodology Committee is charged to develop methodological standards for PCOR. The 4 general areas identified by the committee in which standards will be developed are (1) prioritizing research questions, (2) using appropriate study designs and analyses, (3) incorporating patient perspectives throughout the research continuum, and (4) fostering efficient dissemination and implementation of results. A Congressionally mandated PCORI methodology report (to be issued in its first iteration in May 2012) will begin to provide standards in each of these areas, and will inform future PCORI funding announcements and review criteria. The work of the Methodology Committee is intended to enable generation of information that is relevant and trustworthy for patients, and to enable decisions that improve patient-centered outcomes.

Abstract

MicroRNA (miR) expression may have prognostic value for many types of cancers. However, the miR literature comprises many small studies. We systematically reviewed and synthesized the evidence.Using MEDLINE (last update December 2010), we identified English language studies that examined associations between miRs and cancer prognosis using tumor specimens for more than 10 patients during classifier development. We included studies that assessed a major clinical outcome (nodal disease, disease progression, response to therapy, metastasis, recurrence, or overall survival) in an agnostic fashion using either polymerase chain reaction or hybridized oligonucleotide microarrays.Forty-six articles presenting results on 43 studies pertaining to 20 different types of malignancy were eligible for inclusion in this review. The median study size was 65 patients (interquartile range [IQR] = 34-129), the median number of miRs assayed was 328 (IQR = 250-470), and overall survival or recurrence were the most commonly measured outcomes (30 and 19 studies, respectively). External validation was performed in 21 studies, 20 of which reported at least one nominally statistically significant result for a miR classifier. The median hazard ratio for poor outcome in externally validated studies was 2.52 (IQR = 2.26-5.40). For all classifier miRs in studies that evaluated overall survival across diverse malignancies, the miRs most frequently associated with poor outcome after accounting for differences in miR assessment due to platform type were let-7 (decreased expression in patients with cancer) and miR 21 (increased expression).MiR classifiers show promising prognostic associations with major cancer outcomes and specific miRs are consistently identified across diverse studies and platforms. These types of classifiers require careful external validation in large groups of cancer patients that have adequate protection from bias. -

Abstract

The effectiveness of psychoanalysis and long-term psychoanalytic psychotherapy (LTPP) is debated. We evaluated the effectiveness of LTPP, compared to other treatments or no treatment, in patients with clearly defined metal disorders. We selected randomised or quasi-randomised controlled trials on LTPP. Two authors independently identified trials for inclusion. Eleven trials were eligible. The risk difference for recovery (primary outcome) at the longest available follow-up was 0.00 (95% CI: -0.17 to 0.17; p=0.96; I-squared: 58%). The combined Hedges' g, at the longest follow-up for each study, were: for target problems: -0.05 (95% CI -0.55 to 0.46; p=0.86; I-squared=88%); general psychiatric symptoms: 0.69 (95% CI -0.19 to 1.57; p=0.13; I-squared=96%); personality pathology: 0.17 (95% CI: -0.25 to 0.59; p=0.42; I-squared=41%); social functioning: 0.20 (95% CI -0.10 to 0.50; p=0.19; I-squared=53%); overall effectiveness: 0.33 (95% CI -0.31 to 0.96; p=0.32; I-squared=94%); and quality of life: -0.37 (95% CI: -0.78 to 0.04; p=0.08; I-squared=55%). A subgroup analysis of the domain target problem showed that LTPP did significantly better when compared to control treatments without a specialized psychotherapy component, but not when compared to various specialized psychotherapy control treatments. An exploratory meta-regression indicated that there might be a relation between the difference in treatment intensity between the intervention and control group (session ratio) and effect size. We came to conclude that the recovery rate of various mental disorders was equal after LTPP or various control treatments, including treatment as usual. The effect sizes of the individual trials varied substantially in direction and magnitude. In contrast to previous reviews, we found the evidence for the effectiveness of LTPP to be limited and at best conflicting.

Abstract

More than 800 published genetic association studies have implicated dozens of potential risk loci in Parkinson's disease (PD). To facilitate the interpretation of these findings, we have created a dedicated online resource, PDGene, that comprehensively collects and meta-analyzes all published studies in the field. A systematic literature screen of -27,000 articles yielded 828 eligible articles from which relevant data were extracted. In addition, individual-level data from three publicly available genome-wide association studies (GWAS) were obtained and subjected to genotype imputation and analysis. Overall, we performed meta-analyses on more than seven million polymorphisms originating either from GWAS datasets and/or from smaller scale PD association studies. Meta-analyses on 147 SNPs were supplemented by unpublished GWAS data from up to 16,452 PD cases and 48,810 controls. Eleven loci showed genome-wide significant (P < 5 × 10(-8)) association with disease risk: BST1, CCDC62/HIP1R, DGKQ/GAK, GBA, LRRK2, MAPT, MCCC1/LAMP3, PARK16, SNCA, STK39, and SYT11/RAB25. In addition, we identified novel evidence for genome-wide significant association with a polymorphism in ITGA8 (rs7077361, OR 0.88, P? =? 1.3 × 10(-8)). All meta-analysis results are freely available on a dedicated online database (www.pdgene.org), which is cross-linked with a customized track on the UCSC Genome Browser. Our study provides an exhaustive and up-to-date summary of the status of PD genetics research that can be readily scaled to include the results of future large-scale genetics projects, including next-generation sequencing studies.

Abstract

Systemic corticosteroids have been proposed for numerous indications and there are many claims that corticosteroids can reduce mortality in diverse conditions.We performed an umbrella, agenda-wide review of the evidence on systemic corticosteroids and mortality, focusing primarily on large trials (defined as those with > 100 deaths) and meta-analyses. Searches were performed in PubMed and Cochrane Central Register of Controlled Trials (last update February 2011). We also examined whether spurious subset analyses may be responsible for claims of survival benefits in indications where only small trials had been available.Among 257 identified randomized trials with mortality data in their abstract, we found 14 large trials pertaining to 10 different indications. Although 10 of these 14 trials have reported statistically significant survival differences in subset analyses, none shows a nominally statistically significant (P < 0·05) decrease in death risk for any of the tested conditions when all deaths on all randomized patients are analysed. Meta-analyses for these conditions show statistically significant reductions in mortality only with antenatal corticosteroids for preterm labour (relative risk 0·77, 95% CI, 0·67-0·89) and in tuberculous meningitis (relative risk 0·78, 95% CI, 0·67-0·91). For conditions without any large trials, statistically significant reductions in mortality in meta-analyses were noted for Pneumocystis pneumonia (relative risk 0·54, 95% CI, 0·38-0·79) and alcoholic hepatitis (relative risk 0·63, 95% CI, 0·50-0·80). Many small trials that claim significant benefits, even those for classic indications such as typhoid fever and tetanus, have shown these benefits only in subset analyses.Corticosteroids have been documented to decrease mortality in some indications, in particular, antenatal use for preterm labour, tuberculous meningitis, Pneumocystis pneumonia, and alcoholic hepatitis. Many postulated benefits of corticosteroids on mortality may reflect 'vibration of treatment effects' leading to false-positive claims from spurious subset analyses and even for standard indications, such biases may have inflated the treatment effect estimates. More large trials are needed for serious, common conditions where use of corticosteroids is proposed.

Abstract

Robust replication is a sine qua non for the rigorous documentation of proposed associations in the genome-wide association (GWA) setting. Currently, associations of common variants reaching P ? 5 × 10(-8) are considered replicated. However, there is some ambiguity about the most suitable threshold for claiming genome-wide significance.We defined as 'borderline' associations those with P > 5 × 10(-8) and P ? 1 × 10(-7). The eligible associations were retrieved using the 'Catalog of Published Genome-Wide Association Studies'. For each association we assessed whether it reached P ? 5 × 10(-8) with inclusion of additional data from subsequent GWA studies.Thirty-four eligible genotype-phenotype associations were evaluated with data and clarifications contributed from diverse investigators. Replication data from subsequent GWA studies could be obtained for 26 of them. Of those, 19 associations (73%) reached P ? 5 × 10(-8) for the same or a related trait implicating either the exact same allele or one in very high linkage disequilibrium and 17 reached P < 10(-8). If the seven associations that did not reach P ? 5 × 10(-8) when additional data were considered are assumed to have been false-positives, the false-discovery rate for borderline associations is estimated to be 27% [95% confidence interval (CI) 12-48%]. For five associations, the current P-value is > 10(-6) [corresponding false-discovery rate 19% (95% CI 7-39%)].A substantial proportion, but not all, of the associations with borderline genome-wide significance represent replicable, possibly genuine associations. Our empirical evaluation suggests a possible relaxation in the current GWS threshold.

Abstract

Advances in laboratory techniques have led to a rapidly increasing use of biomarkers in epidemiological studies. Biomarkers of internal dose, early biological change, susceptibility and clinical outcomes are used as proxies for investigating interactions between external and/or endogenous agents and body components or processes. The need for improved reporting of scientific research led to influential statements of recommendations such as the STrengthening Reporting of OBservational studies in Epidemiology (STROBE) statement. The STROBE initiative established in 2004 aimed to provide guidance on how to report observational research. Its guidelines provide a user-friendly checklist of 22 items to be reported in epidemiological studies, with items specific to the three main study designs: cohort studies, case-control studies and cross-sectional studies. The present STrengthening the Reporting of OBservational studies in Epidemiology -Molecular Epidemiology (STROBE-ME) initiative builds on the STROBE statement implementing nine existing items of STROBE and providing 17 additional items to the 22 items of STROBE checklist. The additions relate to the use of biomarkers in epidemiological studies, concerning collection, handling and storage of biological samples; laboratory methods, validity and reliability of biomarkers; specificities of study design; and ethical considerations. The STROBE-ME recommendations are intended to complement the STROBE recommendations.

Abstract

Advances in laboratory techniques have led to a rapidly increasing use of biomarkers in epidemiological studies. Biomarkers of internal dose, early biological change, susceptibility and clinical outcomes are used as proxies for investigating interactions between external and / or endogenous agents and body components or processes. The need for improved reporting of scientific research led to influential statements of recommendations such as the STrengthening Reporting of OBservational studies in Epidemiology (STROBE) statement. The STROBE initiative established in 2004 aimed to provide guidance on how to report observational research. Its guidelines provide a user-friendly checklist of 22 items to be reported in epidemiological studies, with items specific to the three main study designs: cohort studies, case-control studies and cross-sectional studies. The present STrengthening the Reporting of OBservational studies in Epidemiology - Molecular Epidemiology (STROBE-ME) initiative builds on the STROBE statement implementing nine existing items of STROBE and providing 17 additional items to the 22 items of STROBE checklist. The additions relate to the use of biomarkers in epidemiological studies, concerning collection, handling and storage of biological samples; laboratory methods, validity and reliability of biomarkers; specificities of study design; and ethical considerations. The STROBE-ME recommendations are intended to complement the STROBE recommendations.

Abstract

Randomized evidence for vaccine immunogenicity and safety is urgently needed in the setting of pandemics with new emerging infectious agents. We carried out an observational survey to evaluate how many randomized controlled trials testing 2009 H1N1 vaccines were published among those registered, and what was the time lag from their start to publication and from their completion to publication.PubMed, EMBASE and 9 clinical trial registries were searched for eligible randomized controlled trials. The units of the analysis were single randomized trials on any individual receiving influenza vaccines in any setting.73 eligible trials were identified that had been registered in 2009-2010. By June 30, 2011 only 21 (29%) of these trials had been published, representing 38% of the randomized sample size (19905 of 52765). Trials starting later were published less rapidly (hazard ratio 0.42 per month; 95% Confidence Interval: 0.27 to 0.64; p<0.001). Similarly, trials completed later were published less rapidly (hazard ratio 0.43 per month; 95% CI: 0.27 to 0.67; p<0.001). Randomized controlled trials were completed promptly (median, 5 months from start to completion), but only a minority were subsequently published.Most registered randomized trials on vaccines for the H1N1 pandemic are not published in the peer-reviewed literature.

Abstract

"Omics" research poses acute challenges regarding how to enhance validation practices and eventually the utility of this rich information. Several strategies may be useful, including routine replication, public data and protocol availability, funding incentives, reproducibility rewards or penalties, and targeted repeatability checks.

Abstract

Advances in laboratory techniques have led to a rapidly increasing use of biomarkers in epidemiological studies. Biomarkers of internal dose, early biological change susceptibility and clinical outcomes are used as proxies for investigating the interactions between external and/or endogenous agents and body components or processes. The need for improved reporting of scientific research led to influential statements of recommendations such as the STrengthening Reporting of OBservational studies in Epidemiology (STROBE) statement. The STROBE initiative established in 2004 aimed to provide guidance on how to report observational research. Its guidelines provide a user-friendly checklist of 22 items to be reported in epidemiological studies, with items specific to the three main study designs: cohort studies, case-control studies and cross-sectional studies. The present STrengthening the Reporting of OBservational studies in Epidemiology -Molecular Epidemiology (STROBE-ME) initiative builds on the STROBE statement implementing 9 existing items of STROBE and providing 17 additional items to the 22 items of STROBE checklist. The additions relate to the use of biomarkers in epidemiological studies, concerning collection, handling and storage of biological samples; laboratory methods, validity and reliability of biomarkers; specificities of study design; and ethical considerations. The STROBE-ME recommendations are intended to complement the STROBE recommendations.

Abstract

Advances in laboratory techniques have led to a rapidly increasing use of biomarkers in epidemiological studies. Biomarkers of internal dose, early biological change, susceptibility and clinical outcomes are used as proxies for investigating the interactions between external and/or endogenous agents and the body components or processes. The need for improved reporting of scientific research led to influential statements of recommendations such as the STrenghtening Reporting of Observational studies in Epidemiology (STROBE) statement. The STROBE initiative established in 2004 aimed to provide guidance on how to report observational research. Its guidelines provide a user-friendly checklist of 22 items to be reported in epidemiological studies, with items specific to the three main study designs: cohort studies, case-control studies and cross-sectional studies. The present STrengthening the Reporting of OBservational studies in Epidemiology - Molecular Epidemiology (STROBE-ME) initiative builds on the STROBE Statement implementing 9 existing items of STROBE and providing 17 additional items to the 22 items of STROBE checklist. The additions relate to the use of biomarkers in epidemiological studies, concerning collection, handling and storage of biological samples; laboratory methods, validity and reliability of biomarkers; specificities of study design; and ethical considerations. The STROBE-ME recommendations are intended to complement the STROBE recommendations.

Abstract

To compare the reported effect sizes of cardiovascular biomarkers in datasets from observational studies with those in datasets from randomised controlled trials.Review of meta-analyses.Meta-analyses of emerging cardiovascular biomarkers (not part of the Framingham risk score) that included datasets from at least one observational study and at least one randomised controlled trial were identified through Medline (last update, January 2011).Study-specific risk ratios were extracted from all identified meta-analyses and synthesised with random effects for (a) all studies, and (b) separately for observational and for randomised controlled trial populations for comparison.31 eligible meta-analyses were identified. For seven major biomarkers (C reactive protein, non-HDL cholesterol, lipoprotein(a), post-load glucose, fibrinogen, B-type natriuretic peptide, and troponins), the prognostic effect was significantly stronger in datasets from observational studies than in datasets from randomised controlled trials. For five of the biomarkers the effect was less than half as strong in the randomised controlled trial datasets. Across all 31 meta-analyses, on average datasets from observational studies suggested larger prognostic effects than those from randomised controlled trials; from a random effects meta-analysis, the estimated average difference in the effect size was 24% (95% CI 7% to 40%) of the overall biomarker effect.Cardiovascular biomarkers often have less promising results in the evidence derived from randomised controlled trials than from observational studies.

Abstract

Heart defects are the most common congenital abnormalities.We aimed to evaluate in a meta-analysis the screening performance of abnormal ductus venosus (DV) Doppler waveform for detection of congenital heart disease (CHD) in chromosomally normal fetuses.Studies were retrieved from a search of MEDLINE, ISI, SCOPUS and EMBASE (from 1999 to March 2011) using the keywords 'ductus venosus', 'DV', 'chromosomal abnormalities', 'congenital heart disease' and 'nuchal translucency'.We considered all studies that examined the diagnostic performance of DV in the first trimester for CHD in chromosomally normal fetuses. We included studies that were limited to fetuses with increased nuchal translucency (NT), normal NT, and studies that examined fetuses regardless of NT status.Seven studies (n = 50,354) regardless of the NT status, nine studies (n = 2908) with increased NT and seven studies (n = 47,610) with normal NT were included in the meta-analysis. We drew hierarchical summary receiver operating characteristic (HSROC) curves using the parameters of the fitted models.In populations including participants regardless of NT status, the summary sensitivity and specificity of DV for detecting CHD were 50 and 93%, respectively. In participants with increased NT, the summary sensitivity and specificity were 83 and 80%, and in those with normal NT, they were 19 and 96%, respectively.The estimated performance of DV assessment for detection of CHD in chromosomally normal fetuses can be considered in evaluating the potential use and limitations of this screening test.

Abstract

Sepiapterin reductase (SPR) gene is an enzyme which catalyses the final step of tetrahydrobiopterin synthesis (BH4) and was implicated in Parkinson's disease (PD) pathogenesis as a candidate gene for PARK3 locus. A number of studies yielded association of the PARK3 locus with PD, and SPR knockout mice were shown to display parkinsonian features. To evaluate the role of SPR gene polymorphisms in diverse populations in PD, we performed collaborative analyses in the Genetic Epidemiology of Parkinson Disease (GEO-PD) Consortium. A total of 5 single nucleotide polymorphisms (3 in the promoter region and 2 in the 3' untranslated region [UTR]) were genotyped. Fixed as well as random effect models were used to provide summary risk estimates of SPR variants. A total of 19 sites provided data for 6547 cases and 9321 controls. Overall odds ratio estimates varied from 0.92 to 1.01. No overall association with the SPR gene using either fixed effect or random effect model was observed in the studied population. I(2) Metric varied from 0% to 36.2%. There was some evidence for an association for participants of North European/Scandinavian descent with the strongest signal for rs1876487 (odds ratio = 0.82; p value = 0.003). Interestingly, families which were used to map the PARK3 locus, have Scandinavian ancestry suggesting a founder effect. In conclusion, this large association study for the SPR gene revealed no association for PD worldwide. However, taking the initial mapping of the PARK3 into account, the role of a population-specific effect warrants consideration in future studies.

Abstract

The ability to predict death is crucial in medicine, and many relevant prognostic tools have been developed for application in diverse settings. We aimed to evaluate the discriminating performance of predictive tools for death and the variability in this performance across different clinical conditions and studies.We used Medline to identify studies published in 2009 that assessed the accuracy (based on the area under the receiver operating characteristic curve [AUC]) of validated tools for predicting all-cause mortality. For tools where accuracy was reported in 4 or more assessments, we calculated summary accuracy measures. Characteristics of studies of the predictive tools were evaluated to determine if they were associated with the reported accuracy of the tool.A total of 94 eligible studies provided data on 240 assessments of 118 predictive tools. The AUC ranged from 0.43 to 0.98 (median [interquartile range], 0.77 [0.71-0.83]), with only 23 of the assessments reporting excellent discrimination (10%) (AUC, >0.90). For 10 tools, accuracy was reported in 4 or more assessments; only 1 tool had a summary AUC exceeding 0.80. Established tools showed large heterogeneity in their performance across different cohorts (I(2) range, 68%-95%). Reported AUC was higher for tools published in journals with lower impact factor (P = .01), with larger sample size (P = .01), and for those that aimed to predict mortality among the highest-risk patients (P = .002) and among children (P

Abstract

Prestigious journals select for publication studies that are considered most important and informative. We aimed to examine whether high-impact general (HIG) medical journals systematically demonstrate more favourable results for experimental interventions compared with the rest of the literature.We scrutinized systematic reviews of the Cochrane Database (Issue 4, 2009) and meta-analyses published in four general journals (2008-09). Eligible articles included ?1 binary outcome meta-analysis(es) pertaining to effectiveness with ?1 clinical trial(s) published in NEJM, JAMA or Lancet. Effect sizes in trials from NEJM, JAMA or Lancet were compared with those from other trials in the same meta-analyses by deriving summary relative odds ratios (sRORs). Additional analyses examined separately early- and late-published trials in HIG journals and journal-specific effects.A total of 79 meta-analyses including 1043 clinical trials were analysed. Trials in HIG journals had similar effects to trials in other journals, when there was large-scale evidence, but showed more favourable results for experimental interventions when they were small. When HIG trials had less than 40 events, the sROR was 1.64 [95% confidence interval (95% CI): 1.23-2.18). The difference was most prominent when small early trials published in HIG journals were compared with subsequent trials [sROR 2.68 (95% CI: 1.33-5.38)]. Late-published HIG trials showed no consistent inflation of effects. The patterns did not differ beyond chance between NEJM, JAMA or Lancet.Small trials published in the most prestigious journals show more favourable effects for experimental interventions, and this is most prominent for early-published trials in such journals. No effect inflation is seen for large trials.

Abstract

Large studies may identify postulated risk factors and interventions with very small effect sizes. We aimed to assess empirically a large number of statistically significant relative risks (RRs) of tiny magnitude and their interpretation by investigators.RRs in the range between 0.95 and 1.05 were identified in abstracts of articles of cohort studies; articles published in NEJM, JAMA or Lancet; and Cochrane reviews. For each eligible tiny effect and the respective study, we recorded information on study design, participants, risk factor/intervention, outcome, effect estimates, P-values and interpretation by study investigators. We also calculated the probability that each effect lies outside specific intervals around the null (RR interval 0.97-1.03, 0.95-1.05, 0.90-1.10).We evaluated 51 eligible tiny effects (median sample size 112?786 for risk factors and 36?021 for interventions). Most (37/51) appeared in articles published in 2006-10. The effects pertained to nutrition (n?=?19), genetic and other biomarkers (n?=?8), correlates of health care (n?=?8) and diverse other topics (n?=?16) of clinical or public health importance and mostly referred to major clinical outcomes. A total of 15 of the 51 effects were >80% likely to lie outside the RR interval 0.97-1.03, but only 8 were >40% likely to lie outside the RR interval 0.95-1.05 and none was >1.7% likely to lie outside the RR interval 0.90-1.10. The authors discussed at least one concern for 23 effects (small magnitude n?=?19, residual confounding n?=?11, selection bias n?=?1). No concerns were expressed for 28 effects.Statistically significant tiny effects for risk factors and interventions of clinical or public health importance become more common in the literature. Cautious interpretation is warranted, since most of these effects could be eliminated with even minimal biases and their importance is uncertain.

Abstract

To assess whether nominally statistically significant effects in meta-analyses of clinical trials are true and whether their magnitude is inflated.Data from the Cochrane Database of Systematic Reviews 2005 (issue 4) and 2010 (issue 1) were used. We considered meta-analyses with binary outcomes and four or more trials in 2005 with P<0.05 for the random-effects odds ratio (OR). We examined whether any of these meta-analyses had updated counterparts in 2010. We estimated the credibility (true-positive probability) under different prior assumptions and inflation in OR estimates in 2005.Four hundred sixty-one meta-analyses in 2005 were eligible, and 80 had additional trials included by 2010. The effect sizes (ORs) were smaller in the updating data (2005-2010) than in the respective meta-analyses in 2005 (median 0.85-fold, interquartile range [IQR]: 0.66-1.06), even more prominently for meta-analyses with less than 300 events in 2005 (median 0.67-fold, IQR: 0.54-0.96). Mean credibility of the 461 meta-analyses in 2005 was 63-84% depending on the assumptions made. Credibility estimates changed >20% in 19-31 (24-39%) of the 80 updated meta-analyses.Most meta-analyses with nominally significant results pertain to truly nonnull effects, but exceptions are not uncommon. The magnitude of observed effects, especially in meta-analyses with limited evidence, is often inflated.

Abstract

Background The leucine-rich repeat kinase 2 gene (LRRK2) harbours highly penetrant mutations that are linked to familial parkinsonism. However, the extent of its polymorphic variability in relation to risk of Parkinson's disease (PD) has not been assessed systematically. We therefore assessed the frequency of LRRK2 exonic variants in individuals with and without PD, to investigate the role of the variants in PD susceptibility.LRRK2 was genotyped in patients with PD and controls from three series (white, Asian, and Arab-Berber) from sites participating in the Genetic Epidemiology of Parkinson's Disease Consortium. Genotyping was done for exonic variants of LRRK2 that were identified through searches of literature and the personal communications of consortium members. Associations with PD were assessed by use of logistic regression models. For variants that had a minor allele frequency of 0·5% or greater, single variant associations were assessed, whereas for rarer variants information was collapsed across variants.121 exonic LRRK2 variants were assessed in 15?540 individuals: 6995 white patients with PD and 5595 controls, 1376 Asian patients and 962 controls, and 240 Arab-Berber patients and 372 controls. After exclusion of carriers of known pathogenic mutations, new independent risk associations were identified for polymorphic variants in white individuals (M1646T, odds ratio 1·43, 95% CI 1·15-1·78; p=0·0012) and Asian individuals (A419V, 2·27, 1·35-3·83; p=0·0011). A protective haplotype (N551K-R1398H-K1423K) was noted at a frequency greater than 5% in the white and Asian series, with a similar finding in the Arab-Berber series (combined odds ratio 0·82, 0·72-0·94; p=0·0043). Of the two previously reported Asian risk variants, G2385R was associated with disease (1·73, 1·20-2·49; p=0·0026), but no association was noted for R1628P (0·62, 0·36-1·07; p=0·087). In the Arab-Berber series, Y2189C showed potential evidence of risk association with PD (4·48, 1·33-15·09; p=0·012).The results for LRRK2 show that several rare and common genetic variants in the same gene can have independent effects on disease risk. LRRK2, and the pathway in which it functions, is important in the cause and pathogenesis of PD in a greater proportion of patients with this disease than previously believed. These results will help discriminate those patients who will benefit most from therapies targeted at LRRK2 pathogenic activity.Michael J Fox Foundation and National Institutes of Health.

Abstract

Advances in laboratory techniques have led to a rapidly increasing use of biomarkers in epidemiological studies. Biomarkers of internal dose, early biological change, susceptibility, and clinical outcomes are used as proxies for investigating the interactions between external and/or endogenous agents and the body components or processes. The need for improved reporting of scientific research led to influential statements of recommendations such as STrengthening Reporting of Observational studies in Epidemiology (STROBE) statement. The STROBE initiative established in 2004 aimed to provide guidance on how to report observational research. Its guidelines provide a user-friendly checklist of 22 items to be reported in epidemiological studies, with items specific to the three main study designs: cohort studies, case-control studies and cross-sectional studies. The present STrengthening the Reporting of OBservational studies in Epidemiology-Molecular Epidemiology (STROBE-ME) initiative builds on the STROBE Statement implementing 9 existing items of STROBE and providing 17 additional items to the 22 items of STROBE checklist. The additions relate to the use of biomarkers in epidemiological studies, concerning collection, handling and storage of biological samples; laboratory methods, validity and reliability of biomarkers; specificities of study design; and ethical considerations. The STROBE-ME recommendations are intended to complement the STROBE recommendations.

Abstract

Despite significant progress in the identification of genetic loci for age-related macular degeneration (AMD), not all of the heritability has been explained. To identify variants which contribute to the remaining genetic susceptibility, we performed the largest meta-analysis of genome-wide association studies to date for advanced AMD. We imputed 6 036 699 single-nucleotide polymorphisms with the 1000 Genomes Project reference genotypes on 2594 cases and 4134 controls with follow-up replication of top signals in 5640 cases and 52 174 controls. We identified two new common susceptibility alleles, rs1999930 on 6q21-q22.3 near FRK/COL10A1 [odds ratio (OR) 0.87; P = 1.1 × 10(-8)] and rs4711751 on 6p12 near VEGFA (OR 1.15; P = 8.7 × 10(-9)). In addition to the two novel loci, 10 previously reported loci in ARMS2/HTRA1 (rs10490924), CFH (rs1061170, and rs1410996), CFB (rs641153), C3 (rs2230199), C2 (rs9332739), CFI (rs10033900), LIPC (rs10468017), TIMP3 (rs9621532) and CETP (rs3764261) were confirmed with genome-wide significant signals in this large study. Loci in the recently reported genes ABCA1 and COL8A1 were also detected with suggestive evidence of association with advanced AMD. The novel variants identified in this study suggest that angiogenesis (VEGFA) and extracellular collagen matrix (FRK/COL10A1) pathways contribute to the development of advanced AMD.

Abstract

There is increasing interest to make primary data from published research publicly available. We aimed to assess the current status of making research data available in highly-cited journals across the scientific literature.We reviewed the first 10 original research papers of 2009 published in the 50 original research journals with the highest impact factor. For each journal we documented the policies related to public availability and sharing of data. Of the 50 journals, 44 (88%) had a statement in their instructions to authors related to public availability and sharing of data. However, there was wide variation in journal requirements, ranging from requiring the sharing of all primary data related to the research to just including a statement in the published manuscript that data can be available on request. Of the 500 assessed papers, 149 (30%) were not subject to any data availability policy. Of the remaining 351 papers that were covered by some data availability policy, 208 papers (59%) did not fully adhere to the data availability instructions of the journals they were published in, most commonly (73%) by not publicly depositing microarray data. The other 143 papers that adhered to the data availability instructions did so by publicly depositing only the specific data type as required, making a statement of willingness to share, or actually sharing all the primary data. Overall, only 47 papers (9%) deposited full primary raw data online. None of the 149 papers not subject to data availability policies made their full primary data publicly available.A substantial proportion of original research papers published in high-impact journals are either not subject to any data availability policies, or do not adhere to the data availability instructions in their respective journals. This empiric evaluation highlights opportunities for improvement.

Abstract

Although the 2009 (H1N1) influenza pandemic officially ended in August 2010, the virus will probably circulate in future years. Several types of H1N1 vaccines have been tested including various dosages and adjuvants, and meta-analysis is needed to identify the best formulation.We searched MEDLINE, EMBASE, and nine clinical trial registries to April 2011, in any language for randomized clinical trials (RCTs) on healthy children, adolescents, adults and the elderly. Primary outcome was the seroconversion rate according to hemagglutinination-inhibition (HI); secondary outcomes were adverse events. For the primary outcome, we used head-to-head meta-analysis and multiple-treatments meta-analysis.Eighteen RCTs could be included in all primary analyses, for a total of 76 arms (16,725 subjects). After 2 doses, all 2009 H1N1 split/subunit inactivated vaccines were highly immunogenic and overcome CPMP seroconversion criteria. After 1 dose only, all split/subunit vaccines induced a satisfactory immunogenicity (>?=?70%) in adults and adolescents, while only some formulations showed acceptable results for children and elderly (non-adjuvanted at high-doses and oil-in-water adjuvanted vaccines). Vaccines with oil-in-water adjuvants were more immunogenic than both nonadjuvanted and aluminum-adjuvanted vaccines at equal doses and their immunogenicity at doses =?6 µg (even with as little as 1.875 µg of hemagglutinin antigen) was not significantly lower than that achieved after higher doses. Finally, the rate of serious vaccine-related adverse events was low for all 2009 H1N1 vaccines (3 cases, resolved in 10 days, out of 22826 vaccinated subjects). However, mild to moderate adverse reactions were more (and very) frequent for oil-in-water adjuvanted vaccines.Several one-dose formulations might be valid for future vaccines, but 2 doses may be needed for children, especially if a low-dose non-adjuvanted vaccine is used. Given that 15 RCTs were sponsored by vaccine manufacturers, future trials sponsored by non-industry agencies and comparing vaccines using different types of adjuvants are needed.

Abstract

• The rapid and continuing progress in gene discovery for complex diseases is fuelling interest in the potential application of genetic risk models for clinical and public health practice. • The number of studies assessing the predictive ability is steadily increasing, but they vary widely in completeness of reporting and apparent quality. • Transparent reporting of the strengths and weaknesses of these studies is important to facilitate the accumulation of evidence on genetic risk prediction. • A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS), building on the principles established by prior reporting guidelines. • These recommendations aim to enhance the transparency, quality and completeness of study reporting and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct or analysis.

Abstract

• The rapid and continuing progress in gene discovery for complex diseases is fuelling interest in the potential application of genetic risk models for clinical and public health practice. • The number of studies assessing the predictive ability is steadily increasing, but the quality and completeness of reporting vary. • A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS), building on the principles established by prior reporting guidelines. • These recommendations aim to enhance the transparency of study reporting and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct or analysis. • A detailed Explanation and Elaboration document is published as an accompanying article [1].

Abstract

The rapid and continuing progress in gene discovery for complex diseases is fuelling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but they vary widely in completeness of reporting and apparent quality. Transparent reporting of the strengths and weaknesses of these studies is important to facilitate the accumulation of evidence on genetic risk prediction. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS), building on the principles established by prior reporting guidelines. These recommendations aim to enhance the transparency, quality and completeness of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct or analysis.

Abstract

To assess whether there are preferences and avoidances for specific comparisons in a clinical trials agenda.We tested for homophily (preference to compare agents against others in the same class) and co-occurrence (preference or avoidance of specific head-to-head comparisons) in the randomized trials agenda of antifungal agents. We searched MEDLINE and Cochrane Library databases for English language randomized trials evaluating systemic antifungals against Candida or Aspergillus in adults. We extracted data on compared regimens, sample size, publication year, indication (treatment/prophylaxis), and underlying disease.One hundred forty-four trials (74 treatments, 70 prophylaxes) were found (n=27,497 patients). Among polyene and azole groups, agents were compared within the same class more often than across classes (homophily test P<0.001). Lipid amphotericin was compared almost entirely against conventional amphotericin (18 trials), with only three comparisons against azoles. Head-to-head comparisons of newer agents were avoided. Only one of 14 trials for echinocandins compared head-to-head two different echinocandins (P<0.001 for co-occurrence). Of 11 trials on newer azoles, only one compared a newer azole with an echinocandin (P<0.001 for co-occurrence).Trial networks for antifungals show that specific comparisons are preferred and others avoided, generating a potentially biased clinical research agenda.

Abstract

Many studies report volume abnormalities in diverse brain structures in patients with various mental health conditions.To evaluate whether there is evidence for an excess number of statistically significant results in studies of brain volume abnormalities that suggest the presence of bias in the literature.PubMed (articles published from January 2006 to December 2009).Recent meta-analyses of brain volume abnormalities in participants with various mental health conditions vs control participants with 6 or more data sets included, excluding voxel-based morphometry.Standardized effect sizes were extracted in each data set, and it was noted whether the results were "positive" (P

Abstract

The rapid and continuing progress in gene discovery for complex diseases is fueling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but the quality and completeness of reporting varies. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies, building on the principles established by previous reporting guidelines. These recommendations aim to enhance the transparency of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct, or analysis. A detailed Explanation and Elaboration document is published on the EJHG website.

Abstract

Although genetic studies have reported a number of loci associated with cutaneous melanoma (CM) risk, a comprehensive synopsis of genetic association studies published in the field and systematic meta-analysis for all eligible polymorphisms have not been reported.We systematically annotated data from all genetic association studies published in the CM field (n = 145), including data from genome-wide association studies (GWAS), and performed random-effects meta-analyses across all eligible polymorphisms on the basis of four or more independent case-control datasets in the main analyses. Supplementary analyses of three available datasets derived from GWAS and GWAS-replication studies were also done. Nominally statistically significant associations between polymorphisms and CM were graded for the strength of epidemiological evidence on the basis of the Human Genome Epidemiology Network Venice criteria. All statistical tests were two-sided.Forty-two polymorphisms across 18 independent loci evaluated in four or more datasets including candidate gene studies and available GWAS data were subjected to meta-analysis. Eight loci were identified in the main meta-analyses as being associated with a risk of CM (P < .05) of which four loci showed a genome-wide statistically significant association (P < 1 × 10(-7)), including 16q24.3 (MC1R), 20q11.22 (MYH7B/PIGU/ASIP), 11q14.3 (TYR), and 5p13.2 (SLC45A2). Grading of the cumulative evidence by the Venice criteria suggested strong epidemiological credibility for all four loci with genome-wide statistical significance and one additional gene at 9p23 (TYRP1). In the supplementary meta-analyses, a locus at 9p21.3 (CDKN2A/MTAP) reached genome-wide statistical significance with CM and had strong epidemiological credibility.To the best of our knowledge, this is the first comprehensive field synopsis and systematic meta-analysis to identify genes associated with an increased susceptibility to CM.

Abstract

An increasing number of studies evaluate the ability of predictors to change risk stratification and alter medical decisions, i.e. reclassification performance. We examined the reported design and analysis of recent studies of reclassification and the robustness of their claims for improved reclassification.Two independent investigators searched PubMed and citations to the article that introduced the currently most popular reclassification metric (net reclassification index, NRI) to identify studies performing reclassification analysis (January 2006-January 2010). We focused on articles that included any analyses comparing the performance of a baseline predictive model vs the baseline model plus some additional predictor for a prospectively assessed outcome. We recorded information on the baseline model used, outcomes assessed, choice of risk thresholds and features of reclassification analyses.Of 58 baseline models used in 51 eligible papers, only 14 (24%) were previously described, used as described and had same outcomes as originally intended. Calibration was examined in 53% of the studies. Sixteen studies (31%) provided a reference for the choice of risk thresholds and only six used the previously proposed categories or justified the use of alternative thresholds. Only 14 studies (27%) stated that the chosen risk thresholds had different therapeutic intervention implications. NRI was calculated in 38 studies and was smaller in studies with adequately referenced or justified risk thresholds vs others (P?0.0001).Reclassification studies would benefit from more rigorous methodological standards; otherwise claims for improved reclassification may remain spurious.

Abstract

The ratio of false-positive to false-negative findings (FP:FN ratio) is an informative metric that warrants further evaluation. The FP:FN ratio varies greatly across different epidemiologic areas. In genetic epidemiology, it has varied from very high values (possibly even >100:1) for associations reported in candidate-gene studies to very low values (1:100 or lower) for associations with genome-wide significance. The substantial reduction over time in the FP:FN ratio in human genome epidemiology has corresponded to the routine adoption of stringent inferential criteria and comprehensive, agnostic reporting of all analyses. Most traditional fields of epidemiologic research more closely follow the practices of past candidate gene epidemiology, and thus have high FP:FN ratios. Further, FP and FN results do not necessarily entail the same consequences, and their relative importance may vary in different settings. This ultimately has implications for what is the acceptable FP:FN ratio and for how the results of published epidemiologic studies should be presented and interpreted.

Abstract

Genome-wide association studies (GWAS) have identified associations with robust statistical support for influencing breast cancer susceptibility. Most GWAS and replications have been conducted in Northern European populations and to a lesser extent in Asians, and Ashkenazi Jews. It is important to evaluate whether these variants confer risk across different populations and also to assess the magnitude of risk conferred. The aim of this study was to evaluate previously GWAS-identified breast cancer risk variants in the Cypriot population. Eleven GWAS-discovered single nucleotide polymorphisms (SNPs) were analyzed for association with breast cancer in 1,109 Cypriot female breast cancer patients and 1,177 healthy female controls. Four of the 11 SNPs evaluated were found to be nominally significantly associated (P < 0.05) with breast cancer risk in the Cypriot population. Based on estimated power, five associations would be expected to be nominally significant. The correlation coefficient of effect sizes (per-allele odds ratio) between the Cypriot population and the original GWAS populations where these SNPs had been discovered was 0.58 (P = 0.064), while allele frequencies were very similar (r = 0.88, P < 0.001). Overall, we show modest concordance for breast cancer GWAS-discovered alleles and their effect sizes in the Cypriot population. The effects sizes of GWAS-discovered SNPs need to be verified separately in different populations.

Abstract

China is undergoing a rapid transition from a rural to an urban society. This societal change is a consequence of a national drive toward economic prosperity. Rapid urbanization impacts on infrastructure, environmental health and human wellbeing. Unlike many cases of urban expansion, Chinese urbanization has led to containment, rather than to increase, in the spread of infectious diseases. Conversely, the incidence of chronic conditions such as cardiovascular and metabolic diseases has risen, with higher rates occurring in urban regions. This rural-urban gradient in disease incidence seems not to be a reflection simply of more aggressive diagnosis or healthcare access. Other diseases exhibit little rural versus urban differences (e.g., liver cancer or respiratory disease), or even occur at a higher rate in the rural population (e.g., esophageal cancer). This article examines the impact of this changing demographic on environmental health and human wellbeing in China. Lessons learned from epidemiological studies mostly carried out in Europe and the U.S. may not be directly transferable to China. We advocate that there is now a need to establish robust systems of accurate data collection, a Chinese biobank network to facilitate the profiling of human health effects, and relevant randomized controlled trials to identify effective interventions in the Chinese urbanized setting. Such studies could allow for the future implementation of disease-preventive strategies.

Abstract

Black box warnings (BBWs) are the strongest medication-related safety warnings in a drug's labeling information and highlight major risks. Absence of a BBW or asynchronous addition of a BBW among same-class drugs could have major implications.We identified the 20 top-selling drugs in 2008 (10 with BBWs and 10 without BBWs on their label) that belonged to different drug classes. We collected labeling information on all drugs belonging in these 20 classes, and recorded differences in the presence and timing of acquisition of BBWs for same-class drugs.Across the 20 evaluated drug classes, we identified 176 different agents, of which 7 had been withdrawn for safety reasons. The reasons for the withdrawals became BBWs in other same-class agents only in two of the seven cases. Differences were identified in 9 of the 20 classes corresponding to 15 BBWs that were not present in all drugs of the same class. The information for 10 of the 15 different BBWs were included in the labels of same-class drugs as simple warnings or text, while it was absent entirely in 5 BBWs. The median interval from the time the BBW had appeared in another drug of the same class was 66 months.Differences in BBW labeling in same-class drugs are common and shape impressions about the safety of similar agents. BBW labeling needs to become more systematic.

Abstract

Despite over 30,000 publications on proteomics in the last decade, and the accumulation of extensive interesting information on the human proteome in diverse observations, the clinical translation of proteomics to-date has had major setbacks. I review here a roadmap for improving the success rate of clinical proteomics. The roadmap includes steps for improvements that need to be made in analytical tools, discovery, validation, clinical application, and post-clinical application appraisal. It is likely that most if not all of the components that are necessary for clinical success are either readily available, or should be possible to put in place with more rigorous research standards and concerted efforts of the research community, clinicians, and health agencies. Enthusiasm for the clinical impact of proteomics may need to be tempered currently until robust evidence can be obtained, but some clinical successes should eventually be feasible.

Abstract

Discussion on improving the power of genome-wide association studies to identify candidate variants and genes is generally centered on issues of maximizing sample size; less attention is given to the role of phenotype definition and ascertainment. The authors used genome-wide data from patients infected with human immunodeficiency virus type 1 (HIV-1) to assess whether differences in type of population (622 seroconverters vs. 636 seroprevalent subjects) or the number of measurements available for defining the phenotype resulted in differences in the effect sizes of associations between single nucleotide polymorphisms and the phenotype, HIV-1 viral load at set point. The effect estimate for the top 100 single nucleotide polymorphisms was 0.092 (95% confidence interval: 0.074, 0.110) log(10) viral load (log(10) copies of HIV-1 per mL of blood) greater in seroconverters than in seroprevalent subjects. The difference was even larger when the authors focused on chromosome 6 variants (0.153 log(10) viral load) or on variants that achieved genome-wide significance (0.232 log(10) viral load). The estimates of the genetic effects tended to be slightly larger when more viral load measurements were available, particularly among seroconverters and for variants that achieved genome-wide significance. Differences in phenotype definition and ascertainment may affect the estimated magnitude of genetic effects and should be considered in optimizing power for discovering new associations.

Abstract

Many biomarkers are proposed in highly cited studies as determinants of disease risk, prognosis, or response to treatment, but few eventually transform clinical practice.To examine whether the magnitude of the effect sizes of biomarkers proposed in highly cited studies is accurate or overestimated.We searched ISI Web of Science and MEDLINE until December 2010.We included biomarker studies that had a relative risk presented in their abstract. Eligible articles were those that had received more than 400 citations in the ISI Web of Science and that had been published in any of 24 highly cited biomedical journals. We also searched MEDLINE for subsequent meta-analyses on the same associations (same biomarker and same outcome).In the highly cited studies, data extraction was focused on the disease/outcome, biomarker under study, and first reported relative risk in the abstract. From each meta-analysis, we extracted the overall relative risk and the relative risk in the largest study. Data extraction was performed independently by 2 investigators.We evaluated 35 highly cited associations. For 30 of the 35 (86%), the highly cited studies had a stronger effect estimate than the largest study; for 3 the largest study was also the highly cited study; and only twice was the effect size estimate stronger in the largest than in the highly cited study. For 29 of the 35 (83%) highly cited studies, the corresponding meta-analysis found a smaller effect estimate. Only 15 of the associations were nominally statistically significant based on the largest studies, and of those only 7 had a relative risk point estimate greater than 1.37.Highly cited biomarker studies often report larger effect estimates for postulated associations than are reported in subsequent meta-analyses evaluating the same associations.

Abstract

Clinical practice guidelines are important for guiding practice, but it is unclear if they are commensurate with the available evidence.We examined guidelines produced by cancer and gynecological societies and organizations and evaluated their coverage of and stance towards chemotherapy for advanced stage disease among 4 gynecological malignancies (breast, ovarian, cervical, endometrial cancer) where the evidence for the use of chemotherapy is very different (substantial and conclusive for breast and ovarian cancer, limited and suggesting no major benefit for cervical and endometrial cancer). Eligible societies and organizations were identified through systematic internet searches (last update June 2009). Pertinent websites were scrutinized for presence of clinical practice guidelines, and relative guidelines were analyzed.Among 224 identified eligible societies and organizations, 69 (31%) provided any sort of guidelines, while recommendations for chemotherapy on advanced stage gynecological malignancies were available in 20 of them. Only 14 had developed their own guideline, and only 5 had developed guidelines for all 4 malignancies. Use of levels of evidence and grades of recommendations, and aspects of the production, implementation, and timeliness of the guidelines did not differ significantly across malignancies. Guidelines on breast and ovarian cancer utilized significantly more randomized trials and meta-analyses. Guidelines differed across malignancies on their coverage of disease-free survival (p?=?0.033), response rates (p?=?0.024), symptoms relief (p?=?0.005), quality of life (p?=?0.001) and toxicity (p?=?0.039), with breast and ovarian cancer guidelines typically covering more frequently these outcomes. All guidelines explicitly or implicitly endorsed the use of chemotherapy.Clinical practice guidelines are provided by the minority of professional societies and organizations. Available guidelines tend to recommend chemotherapy even for diseases where the effect of chemotherapy is controversial and recommendations are based on scant evidence.

Abstract

We studied the independent and joint effects of the genes encoding alpha-synuclein (SNCA) and microtubule-associated protein tau (MAPT) in Parkinson disease (PD) as part of a large meta-analysis of individual data from case-control studies participating in the Genetic Epidemiology of Parkinson's Disease (GEO-PD) consortium.Participants of Caucasian ancestry were genotyped for a total of 4 SNCA (rs2583988, rs181489, rs356219, rs11931074) and 2 MAPT (rs1052553, rs242557) single nucleotide polymorphism (SNPs). Individual and joint effects of SNCA and MAPT SNPs were investigated using fixed- and random-effects logistic regression models. Interactions were studied on both a multiplicative and an additive scale, and using a case-control and case-only approach.Fifteen GEO-PD sites contributed a total of 5,302 cases and 4,161 controls. All 4 SNCA SNPs and the MAPT H1-haplotype-defining SNP (rs1052553) displayed a highly significant marginal association with PD at the significance level adjusted for multiple comparisons. For SNCA, the strongest associations were observed for SNPs located at the 3' end of the gene. There was no evidence of statistical interaction between any of the 4 SNCA SNPs and rs1052553 or rs242557, neither on the multiplicative nor on the additive scale.This study confirms the association between PD and both SNCA SNPs and the H1 MAPT haplotype. It shows, based on a variety of approaches, that the joint action of variants in these 2 loci is consistent with independent effects of the genes without additional interacting effects.

Abstract

Proposed molecular classifiers may be overfit to idiosyncrasies of noisy genomic and proteomic data. Cross-validation methods are often used to obtain estimates of classification accuracy, but both simulations and case studies suggest that, when inappropriate methods are used, bias may ensue. Bias can be bypassed and generalizability can be tested by external (independent) validation. We evaluated 35 studies that have reported on external validation of a molecular classifier. We extracted information on study design and methodological features, and compared the performance of molecular classifiers in internal cross-validation versus external validation for 28 studies where both had been performed. We demonstrate that the majority of studies pursued cross-validation practices that are likely to overestimate classifier performance. Most studies were markedly underpowered to detect a 20% decrease in sensitivity or specificity between internal cross-validation and external validation [median power was 36% (IQR, 21-61%) and 29% (IQR, 15-65%), respectively]. The median reported classification performance for sensitivity and specificity was 94% and 98%, respectively, in cross-validation and 88% and 81% for independent validation. The relative diagnostic odds ratio was 3.26 (95% CI 2.04-5.21) for cross-validation versus independent validation. Finally, we reviewed all studies (n = 758) which cited those in our study sample, and identified only one instance of additional subsequent independent validation of these classifiers. In conclusion, these results document that many cross-validation practices employed in the literature are potentially biased and genuine progress in this field will require adoption of routine external validation of molecular classifiers, preferably in much larger studies than in current practice.

Abstract

The objective of this study was to assess the reporting of studies on new prognostic markers of outcome in acute pancreatitis.We used MEDLINE searches complemented with perusal of review articles' references to identify eligible English-language studies. We included studies evaluating nonroutine markers for acute pancreatitis. Eligible outcomes included Atlanta criteria, Japanese criteria for severity, multiple/single organ failure, complications, interventional treatment, hospitalization length, and death. We generated a 47-item checklist on Acute Pancreatitis Prognosis by adapting a previously constructed reporting guidance instrument for prognostic tumor markers (REMARK [Reporting Recommendations for Tumor Marker Prognostic Studies]). The checklist addresses the reporting of essential information in prognostic studies.The 184 identified eligible studies reported on 196 different prognostic markers. One hundred forty-four studies (78.3%) found at least 1 prognostic marker to be nominally statistically significant. Significant improvements over time were seen in the reporting for 17 items, but major deficiencies were noted even in 2004-2009 studies. Particularly, 12 items were reported in less than 10% of studies overall and even within the most recent studies.Despite some improvements over time, the reporting of important aspects of prognostic studies in acute pancreatitis remains suboptimal. The proposed REMARK-based checklist may help improve the quality and reporting of research in this field.

Abstract

The genetic aetiology of osteoarthritis has not yet been elucidated. To enable a well-powered genome-wide association study (GWAS) for osteoarthritis, the authors have formed the arcOGEN Consortium, a UK-wide collaborative effort aiming to scan genome-wide over 7500 osteoarthritis cases in a two-stage genome-wide association scan. Here the authors report the findings of the stage 1 interim analysis.The authors have performed a genome-wide association scan for knee and hip osteoarthritis in 3177 cases and 4894 population-based controls from the UK. Replication of promising signals was carried out in silico in five further scans (44,449 individuals), and de novo in 14 534 independent samples, all of European descent.None of the association signals the authors identified reach genome-wide levels of statistical significance, therefore stressing the need for corroboration in sample sets of a larger size. Application of analytical approaches to examine the allelic architecture of disease to the stage 1 genome-wide association scan data suggests that osteoarthritis is a highly polygenic disease with multiple risk variants conferring small effects.Identifying loci conferring susceptibility to osteoarthritis will require large-scale sample sizes and well-defined phenotypes to minimise heterogeneity.

Abstract

The rapid and continuing progress in gene discovery for complex diseases is fueling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but they vary widely in completeness of reporting and apparent quality. Transparent reporting of the strengths and weaknesses of these studies is important to facilitate the accumulation of evidence on genetic risk prediction. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS), building on the principles established by previous reporting guidelines. These recommendations aim to enhance the transparency, quality and completeness of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct or analysis.

Abstract

Industry involvement has been associated with more favourable cost-effectiveness ratios in cost-effectiveness analyses, but the mechanisms for this association are unclear. We evaluated whether the assumed accuracy of the Papanicolaou (Pap) test was correlated with the features of cost-effectiveness analysis studies.We searched PubMed (last updated April 2010) for cost-effectiveness analysis studies in which at least one strategy involved the Pap test for cervical cancer. We assessed the baseline assumed diagnostic sensitivity and specificity of the Pap test in each study and the association of these values with three levels of manufacturer involvement in the study.Among 88 analyzed cost-effectiveness analysis studies, the assumed sensitivity of the Pap test was lower in studies with manufacturer-affiliated authors, manufacturer funding or manufacturer-related competing interests versus studies without (mean sensitivity 60% v. 70%, p < 0.001). The assumed specificity of the Pap test was lower in cost-effectiveness analyses involving new screening tests (mean 93% v. 96%, p = 0.016). The assumed specificity did not differ between trials with manufacturer involvement versus those without (mean 95% v. 95%, p = 0.755).The results of cost-effectiveness analyses may be affected by a downgrading of the assumed diagnostic accuracy of the standard Pap test against which newer tests or interventions are compared. New technology then seems to have more favourable results against a straw-man comparator.

Abstract

The rapid and continuing progress in gene discovery for complex diseases is fuelling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but they vary widely in completeness of reporting and apparent quality. Transparent reporting of the strengths and weaknesses of these studies is important to facilitate the accumulation of evidence on genetic risk prediction. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS), building on the principles established by prior reporting guidelines. These recommendations aim to enhance the transparency, quality and completeness of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct or analysis.

Abstract

The rapid and continuing progress in gene discovery for complex diseases is fueling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but the quality and completeness of reporting varies. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS), building on the principles established by prior reporting guidelines. These recommendations aim to enhance the transparency of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct, or analysis. A detailed Explanation and Elaboration document is published.

Abstract

To present the systematic literature review (SLR), which formed the basis for the European League Against Rheumatism (EULAR) evidence-based recommendations for vaccination in adult patients with auto-immune inflammatory rheumatic diseases (AIIRD).AIIRD, vaccines and immunomodulating drugs, as well as eight key questions were defined by the multidisciplinary expert committee commissioned by EULAR for developing the recommendations. A SLR was performed using MedLine through October 2009 and including data from meta-analyses, systematic reviews, randomized trials, and observational studies, excluding case series with ? 5 participants. Articles in English and regarding patients ? 16 years of age, were eligible.Several vaccine-preventable infections (VPI) occur more often in AIIRD-patients and most vaccines are efficacious in AIIRD-patients, even when treated with immunomodulating agents, except rituximab. There does not appear to be an increase in vaccination-related harms in vaccinated patients with AIIRD in comparison with unvaccinated patients with AIIRD. However, these studies are underpowered and therefore not conclusive.Based on the current evidence from the literature, recommendations for vaccination in patients with AIIRD were made. However, more research is needed in particular regarding incidence of VPI, harms of vaccination and the influence of (new and established) immunomodulating agents on vaccination efficacy.

Abstract

Meta-analyses play an important role in synthesizing evidence from diverse studies and datasets that address similar questions. A major obstacle for meta-analyses arises from biases in reporting. In particular, it is speculated that findings which do not achieve formal statistical significance are less likely reported than statistically significant findings. Moreover, the patterns of bias can be complex and may also depend on the timing of the research results and their relationship with previously published work. In this paper, we present an approach that is specifically designed to analyze large-scale datasets on published results. Such datasets are currently emerging in diverse research fields, particularly in molecular medicine. We use our approach to investigate a dataset on Alzheimer's disease (AD) that covers 1167 results from case-control studies on 102 genetic markers. We observe that initial studies on a genetic marker tend to be substantially more biased than subsequent replications. The chances for initial, statistically non-significant results to be published are estimated to be about 44% (95% CI, 32% to 63%) relative to statistically significant results, while statistically non-significant replications have almost the same chance to be published as statistically significant replications (84%; 95% CI, 66% to 107%). Early replications tend to be biased against initial findings, an observation previously termed Proteus phenomenon: The chances for non-significant studies going in the same direction as the initial result are estimated to be lower than the chances for non-significant studies opposing the initial result (73%; 95% CI, 55% to 96%). Such dynamic patterns in bias are difficult to capture by conventional methods, where typically simple publication bias is assumed to operate. Our approach captures and corrects for complex dynamic patterns of bias, and thereby helps generating conclusions from published results that are more robust against the presence of different coexisting types of selective reporting.

Abstract

To develop evidence-based European League Against Rheumatism (EULAR) recommendations for vaccination in patients with autoimmune inflammatory rheumatic diseases (AIIRD).A EULAR task force was composed of experts representing 11 European countries, consisting of eight rheumatologists, four clinical immunologists, one rheumatologist/clinical immunologist, one infectious disease physician, one nephrologist, one paediatrician/rheumatologist and one clinical epidemiologist. Key questions were formulated and the eligible spectrum of AIIRD, immunosuppressive drugs and vaccines were defined in order to perform a systematic literature review. A search was made of Medline from 1966 to October 2009 as well as abstracts from the EULAR meetings of 2008 and 2009 and the American College of Rheumatology (ACR) meetings of 2007 and 2008. Evidence was graded in categories I-IV, the strength of recommendations was graded in categories A-D and Delphi voting was applied to determine the level of agreement between the experts of the task force.Eight key questions and 13 recommendations addressing vaccination in patients with AIIRD were formulated. The strength of each recommendation was determined. Delphi voting revealed a very high level of agreement with the recommendations among the experts of the task force. Finally, a research agenda was proposed.Recommendations for vaccination in patients with AIIRD based on the currently available evidence and expert opinion were formulated. More research is needed, particularly regarding the incidence of vaccine-preventable infectious diseases and the safety of vaccination in patients with AIIRD.

Abstract

A meta-analysis of studies was conducted involving 24,511 participants with 7,864 fractures in which polymorphisms in the 5' flank of COL1A1 (rs1107946, rs2412298, and rs1800012) were related to osteoporosis phenotypes. Polymorphisms of all three sites were associated with BMD, and rs1800012 was associated with fracture but effect sizes were modest.Polymorphisms in the 5' flank of COL1A1 gene have been implicated as genetic markers for susceptibility to osteoporosis, but previous studies have yielded conflicting results.We conducted a meta-analysis of 32 studies including 24,511 participants and 7,864 fractures in which alleles at the -1997G/T (rs1107946), -1663in/delT (rs2412298), and Sp1 binding site polymorphisms (rs1800012) of COL1A1 had been related to bone mineral density (BMD) or fracture.For the Sp1 polymorphism, BMD values in TT homozygotes were 0.13 units [95% CI, 0.03 to 0.24] lower at the spine (p?=?0.01) and 0.16 units [0.10 to 0.23] lower at the hip (p = 1 x 10??) than GG homozygotes. Clinical fractures were 1.31-fold [1.04-1.65] increased in TT homozygotes (p?=?0.02) and vertebral fractures were 1.34-fold [1.01-1.77] increased (p?=?0.04). We also observed associations between spine BMD and allelic variants at the -1997G/T (p?=?0.05) and the -1663indelT (p?=?0.009) sites. We found no association between alleles at the -1997G/T or -1663indelT sites and fracture but power was limited.The COL1A1 Sp1 polymorphism is associated with a modest reduction in BMD and an increased risk of fracture, although we cannot fully exclude the possibility that the results may have been influenced by publication bias. Further studies are required to fully evaluate the contribution of the -1997G/T and -1663in/delT sites to these phenotypes and to determine if they interact with the Sp1 polymorphism to regulate susceptibility to osteoporosis.

Abstract

High-profile studies have provided conflicting results regarding the involvement of the Omi/HtrA2 gene in Parkinson's disease (PD) susceptibility. Therefore, we performed a large-scale analysis of the association of common Omi/HtrA2 variants in the Genetic Epidemiology of Parkinson's disease (GEO-PD) consortium. GEO-PD sites provided clinical and genetic data including affection status, gender, ethnicity, age at study, age at examination (all subjects); age at onset and family history of PD (patients). Genotyping was performed for the five most informative SNPs spanning the Omi/HtrA2 gene in approximately 2-3 kb intervals (rs10779958, rs2231250, rs72470544, rs1183739, rs2241028). Fixed as well as random effect models were used to provide summary risk estimates of Omi/HtrA2 variants. The 20 GEO-PD sites provided data for 6378 cases and 8880 controls. No overall significant associations for the five Omi/HtrA2 SNPs and PD were observed using either fixed effect or random effect models. The summary odds ratios ranged between 0.98 and 1.08 and the estimates of between-study heterogeneity were not large (non-significant Q statistics for all 5 SNPs; I(2) estimates 0-28%). Trends for association were seen for participants of Scandinavian descent for rs2241028 (OR 1.41, p=0.04) and for rs1183739 for age at examination (cut-off 65 years; OR 1.17, p=0.02), but these would not be significant after adjusting for multiple comparisons and their Bayes factors were only modest. This largest association study performed to define the role of any gene in the pathogenesis of Parkinson's disease revealed no overall strong association of Omi/HtrA2 variants with PD in populations worldwide.

Re: Fruit and Vegetable Intake and Overall Cancer Risk in the European Prospective Investigation Into Cancer and NutritionJOURNAL OF THE NATIONAL CANCER INSTITUTEIoannidis, J. P., Siontis, G. C.2011; 103 (3)

Abstract

To present some simple graphical and quantitative ways to assist interpretation and improve presentation of results from multiple-treatment meta-analysis (MTM).We reanalyze a published network of trials comparing various antiplatelet interventions regarding the incidence of serious vascular events using Bayesian approaches for random effects MTM, and we explore the advantages and drawbacks of various traditional and new forms of quantitative displays and graphical presentations of results.We present the results under various forms, conventionally based on the mean of the distribution of the effect sizes; based on predictions; based on ranking probabilities; and finally, based on probabilities to be within an acceptable range from a reference. We show how to obtain and present results on ranking of all treatments and how to appraise the overall ranks.Bayesian methodology offers a multitude of ways to present results from MTM models, as it enables a natural and easy estimation of all measures based on probabilities, ranks, or predictions.

Abstract

Osteoarthritis (OA) is the most prevalent form of arthritis and accounts for substantial morbidity and disability, particularly in older people. It is characterised by changes in joint structure, including degeneration of the articular cartilage, and its aetiology is multifactorial with a strong postulated genetic component.A meta-analysis was performed of four genome-wide association (GWA) studies of 2371 cases of knee OA and 35 909 controls in Caucasian populations. Replication of the top hits was attempted with data from 10 additional replication datasets.With a cumulative sample size of 6709 cases and 44 439 controls, one genome-wide significant locus was identified on chromosome 7q22 for knee OA (rs4730250, p=9.2 × 10??), thereby confirming its role as a susceptibility locus for OA.The associated signal is located within a large (500 kb) linkage disequilibrium block that contains six genes: PRKAR2B (protein kinase, cAMP-dependent, regulatory, type II, ?), HPB1 (HMG-box transcription factor 1), COG5 (component of oligomeric golgi complex 5), GPR22 (G protein-coupled receptor 22), DUS4L (dihydrouridine synthase 4-like) and BCAP29 (B cell receptor-associated protein 29). Gene expression analyses of the (six) genes in primary cells derived from different joint tissues confirmed expression of all the genes in the joint environment.

Abstract

An invasive approach is superior to medical management for the treatment of patients with acute coronary syndromes without ST-segment elevation (NSTE-ACS), but the optimal timing of coronary angiography and subsequent intervention, if indicated, has not been settled.We conducted a meta-analysis of randomized trials addressing the optimal timing (early vs. delayed) of coronary angiography in NSTE-ACS. Four trials with 4013 patients were eligible (ABOARD, ELISA, ISAR-COOL, TIMACS), and data for longer follow-up periods than those published became available for this meta-analysis by the ELISA and ISAR-COOL investigators. The median time from admission or randomization to coronary angiography ranged from 1.16 to 14 h in the early and 20.8-86 h in the delayed strategy group. No statistically significant difference of risk of death [random effects risk ratio (RR) 0.85, 95% confidence interval (CI) 0.64-1.11] or myocardial infarction (MI) (RR 0.94, 95% CI 0.61-1.45) was detected between the two strategies. Early intervention significantly reduced the risk for recurrent ischaemia (RR 0.59, 95% CI 0.38-0.92, P = 0.02) and the duration of hospital stay (by 28%, 95% CI 22-35%, P < 0.001). Furthermore, decreased major bleeding events (RR 0.78, 95% CI 0.57-1.07, P = 0.13), and less major events (death, MI, or stroke) (RR 0.91, 95% CI 0.82-1.01, P = 0.09) were observed with the early strategy but these differences were not nominally significant.Early coronary angiography and potential intervention reduces the risk of recurrent ischaemia, and shortens hospital stay in patients with NSTE-ACS.

Abstract

The rapid and continuing progress in gene discovery for complex diseases is fueling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but the quality and completeness of reporting varies. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of genetic risk prediction studies (the GRIPS statement), building on the principles established by prior reporting guidelines. These recommendations aim to enhance the transparency of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct, or analysis. A detailed Explanation and Elaboration document is published at http://www.plosmedicine.org.

Abstract

Multiple studies have evaluated diverse allergens in paediatric populations. Consensus is still lacking on which allergens are most commonly implicated in allergic contact dermatitis.To evaluate the proportion of positive reactions for allergens tested in children and to identify allergens with positive reactions in at least 1% of them.This was a systematic review of studies in PubMed (1966-2010) investigating allergens in at least 100 enrolled children. Proportions of positive reactions for each allergen were combined with random effects models across studies.We included 49 studies with available data on 170 allergens. Each study tested a median of two allergens. Among the 94 allergens evaluated by at least two studies, 58 had estimates of positive reactions of at least 1% by random effects calculations, and for 21 of them the 95% confidence interval ensured that the proportion of positive reactions was at least 1%. The top five allergens tested by at least two studies included nickel sulfate, ammonium persulfate, gold sodium thiosulfate, thimerosal, and toluene-2,5-diamine (p-toluenediamine). For most allergens, the proportion of positive reactions was higher in studies published after 1995 than in earlier studies (p = 0.0065).This meta-analysis offers guidance on which allergens are most prevalent in the paediatric population and should have priority for inclusion in standardized allergen series.

Abstract

The rapidly growing number of molecular epidemiology studies is providing an enormous, often multidimensional, body of evidence on the association of various disease outcomes and biomarkers. The testing and validation of statistical hypotheses in genetic and molecular epidemiology presents a major challenge requiring methodological rigor and analytical power. The non-replication of many genetic and other biomarker association studies suggests that there may be an abundance of spurious findings in the field. This chapter will discuss ways of combining evidence from different sources using meta-analysis methods. Research synthesis not only aims at producing a summary effect estimate for a specific biomarker, but also offers a unique opportunity for a meticulous attempt to critically appraise a research field, identify substantial differences between or within studies, and detect sources of bias. Systematic reviews and meta-analyses in human genome epidemiology are specifically discussed, as they comprise the bulk of the available evidence in molecular epidemiology where these methods have been applied to date. Considered here are issues regarding validity and interpretation in genetic association studies, as well as strategies for developing and integrating evidence through international consortia. Finally, there is a brief look at how combining data through meta-analysis may be applied in other areas of molecular epidemiology.

Is there a glass ceiling for highly cited scientists at the top of research universities?FASEB JOURNALIoannidis, J. P.2010; 24 (12): 4635-4638

Abstract

University leaders aim to protect, shape, and promote the missions of their institutions. I evaluated whether top highly cited scientists are likely to occupy these positions. Of the current leaders of 96 U.S. high research activity universities, only 6 presidents or chancellors were found among the 4009 U.S. scientists listed in the ISIHighlyCited.com database. Of the current leaders of 77 UK universities, only 2 vice-chancellors were found among the 483 UK scientists listed in the same database. In a sample of 100 top-cited clinical medicine scientists and 100 top-cited biology and biochemistry scientists, only 1 and 1, respectively, had served at any time as president of a university. Among the leaders of 25 U.S. universities with the highest citation volumes, only 12 had doctoral degrees in life, natural, physical or computer sciences, and 5 of these 12 had a Hirsch citation index m < 1.0. The participation of highly cited scientists in the top leadership of universities is limited. This could have consequences for the research and overall mission of universities.

Abstract

The agnostic screening performed by genome-wide association studies (GWAS) has uncovered associations for previously unsuspected genes. Knowledge about the functional role of these genes is crucial and laboratory mouse models can provide such information. Here, we describe a systematic juxtaposition of human GWAS-discovered loci versus mouse models in order to appreciate the availability of mouse models data, to gain biological insights for the role of these genes and to explore the extent of concordance between these two lines of evidence. We perused publicly available data (NHGRI database for human associations and Mouse Genome Informatics database for mouse models) and employed two alternative approaches for cross-species comparisons, phenotype- and gene-centric. A total of 293 single gene-phenotype human associations (262 unique genes and 69 unique phenotypes) were evaluated. In the phenotype-centric approach, we identified all mouse models and related ortholog genes for the 51 human phenotypes with a comparable phenotype in mice. A total of 27 ortholog genes were found to be associated with the same phenotype in humans and mice, a concordance that was significantly larger than expected by chance (p<0.001). In the gene-centric approach, we were able to locate at least 1 knockout model for 60% of the 262 genes. The knockouts for 35% of these orthologs displayed pre- or post-natal lethality. For the remaining non-lethal orthologs, the same organ system was involved in mice and humans in 71% of the cases (p<0.001). Our project highlights the wealth of available information from mouse models for human GWAS, catalogues extensive information on plausible physiologic implications for many genes, provides hypothesis-generating findings for additional GWAS analyses and documents that the concordance between human and mouse genetic association is larger than expected by chance and can be informative.

Abstract

Many different types of bias have been described. Some biases may tend to coexist or be associated with specific research settings, fields, and types of studies. We aimed to map systematically the terminology of bias across biomedical research.We used advanced text-mining and clustering techniques to evaluate 17,265,924 items from PubMed (1958-2008). We considered 235 bias terms and 103 other terms that appear commonly in articles dealing with bias.Forty bias terms were used in the title or abstract of more than 100 articles each. Pseudo-inclusion clustering identified 252 clusters of terms. The clusters were organized into macroscopic maps that cover a continuum of research fields. The resulting maps highlight which types of biases tend to co-occur and may need to be considered together and what biases are commonly encountered and discussed in specific fields. Most of the common bias terms have had continuous use over time since their introduction, and some (in particular confounding, selection bias, response bias, and publication bias) show increased usage through time.This systematic mapping offers a dynamic classification of biases in biomedical investigation and related fields and can offer insights for the multifaceted aspects of bias.

Abstract

The authors survey uncommon variants (minor allele frequency, ?5%) that have reached genome-wide significance (P ? 10??) in genome-wide association study(ies) (GWAS). They examine the typical effect sizes of these associations; whether they have arisen in multiple GWAS on the same phenotype; and whether they pertain to genetic loci that have other variants discovered through GWAS, perceived biologic plausibility from the candidate gene era, or known mutations associated with related phenotypes. Forty-three associations with minor allele frequency of 5% or less and P ? 10?? were studied, 12 of which involved nonsynonymous variants. Per-allele odds ratios ranged from 1.03 to 22.11. Thirty-two associations had P ? 10??. Eight uncommon variants were identified in multiple GWAS. For 14 associations, also other common polymorphisms with genome-wide significance were identified in the same loci. Thirteen associations pertained to genetic loci considered to have biologic plausibility for association in the candidate gene era, and mutations with related phenotypic effects were identified for 11 associations. Twenty-five uncommon variants are common in at least 1 of the 4 different ancestry samples of the International HapMap Project. Although the number of uncommon variants with genome-wide significance is still limited, these data suggest a possible confluence of rare/uncommon and common genetic variation on the same genetic loci.

Abstract

Multiple-treatments meta-analyses are increasingly used to evaluate the relative effectiveness of several competing regimens. In some fields which evolve with the continuous introduction of new agents over time, it is possible that in trials comparing older with newer regimens the effectiveness of the latter is exaggerated. Optimism bias, conflicts of interest and other forces may be responsible for this exaggeration, but its magnitude and impact, if any, needs to be formally assessed in each case. Whereas such novelty bias is not identifiable in a pair-wise meta-analysis, it is possible to explore it in a network of trials involving several treatments. To evaluate the hypothesis of novel agent effects and adjust for them, we developed a multiple-treatments meta-regression model fitted within a Bayesian framework. When there are several multiple-treatments meta-analyses for diverse conditions within the same field/specialty with similar agents involved, one may consider either different novel agent effects in each meta-analysis or may consider the effects to be exchangeable across the different conditions and outcomes. As an application, we evaluate the impact of modelling and adjusting for novel agent effects for chemotherapy and other non-hormonal systemic treatments for three malignancies. We present the results and the impact of different model assumptions to the relative ranking of the various regimens in each network. We established that multiple-treatments meta-regression is a good method for examining whether novel agent effects are present and estimation of their magnitude in the three worked examples suggests an exaggeration of the hazard ratio by 6 per cent (2-11 per cent).

Abstract

There is an overwhelming abundance of genetic association studies available in the literature, which can often be collectively difficult to interpret. To address this issue, the Venice interim guidelines were established for determining the credibility of the cumulative evidence. The objective of this report is to evaluate the literature on the association of common glutathione S-transferase (GST) variants (GSTM1 null, GSTT1 null and GSTP1 Ile105Val polymorphism) and lung cancer, and to assess the credibility of the associations using the newly proposed cumulative evidence guidelines.Information from the literature was enriched with an updated meta-analysis and a pooled analysis using data from the Genetic Susceptibility to Environmental Carcinogens database.There was a significant association between GSTM1 null and lung cancer for the meta-analysis (meta odds ratio=1.17, 95% confidence interval: 1.10-1.25) and pooled analysis (adjusted odds ratio=1.10, 95% confidence interval: 1.04-1.16), although substantial heterogeneity was present. No overall association between lung cancer and GSTT1 null or GSTP1 Ile105Val was found. When the Venice criteria was applied, cumulative evidence for all associations were considered 'weak', with the exception of East Asian carriers of the G allele of GSTP1 Ile105Val, which was graded as 'moderate' evidence.Despite the large amounts of studies, and several statistically significant summary estimates produced by meta-analyses, the application of the Venice criteria suggests extensive heterogeneity and susceptibility to bias for the studies on association of common genetic polymorphisms, such as with GST variants and lung cancer.

Abstract

Recent emphasis on translational research (TR) is highlighting the role of epidemiology in translating scientific discoveries into population health impact. The authors present applications of epidemiology in TR through 4 phases designated T1-T4, illustrated by examples from human genomics. In T1, epidemiology explores the role of a basic scientific discovery (e.g., a disease risk factor or biomarker) in developing a "candidate application" for use in practice (e.g., a test used to guide interventions). In T2, epidemiology can help to evaluate the efficacy of a candidate application by using observational studies and randomized controlled trials. In T3, epidemiology can help to assess facilitators and barriers for uptake and implementation of candidate applications in practice. In T4, epidemiology can help to assess the impact of using candidate applications on population health outcomes. Epidemiology also has a leading role in knowledge synthesis, especially using quantitative methods (e.g., meta-analysis). To explore the emergence of TR in epidemiology, the authors compared articles published in selected issues of the Journal in 1999 and 2009. The proportion of articles identified as translational doubled from 16% (11/69) in 1999 to 33% (22/66) in 2009 (P = 0.02). Epidemiology is increasingly recognized as an important component of TR. By quantifying and integrating knowledge across disciplines, epidemiology provides crucial methods and tools for TR.

Abstract

Diverse methods of large-scale measurements of biological processes have emerged in the last 15 years and their list is growing rapidly. Almost invariably, these advances in omics have been associated with major expectations of transforming not only biological knowledge but also medicine and health. However, practical applications of omics in biomedicine have often suffered from poor attention to issues of validity. As a consequence, major promises of personalized medicine have not yet materialized in improving patient or population outcomes. Several omics fields increasingly realize the need to safeguard the validity of their efforts, make reporting more transparent, and improve the translational potential of their studies. Many discoveries point indeed toward a highly individualized profile of health and disease, where each case is different, but this is currently difficult to translate into more effective personalized treatment or prevention. Given the exponential growth of collected data, understanding is often drowning in the sea of measurements.

Abstract

Influential medical journals shape medical science and practice and their prestige is usually appraised by citation impact metrics, such as the journal impact factor. However, how permanent are medical journals and how stable is their impact over time?We evaluated what happened to general medical journals that were publishing papers half a century ago, in 1959. Data were retrieved from ISI Web of Science for citations and PubMed (Journals function) for journal history. Of 27 eligible journals publishing in 1959, 4 have stopped circulation (including two of the most prestigious journals in 1959) and another 7 changed name between 1959 and 2009. Only 6 of these 27 journals have been published continuously with their initial name since they started circulation. The citation impact of papers published in 1959 gives a very different picture from the current journal impact factor; the correlation between the two is non-significant and very close to zero. Only 13 of the 5,223 papers published in 1959 received at least 5 citations in 2009.Journals are more permanent entities than single papers, but they are also subject to major change and their relative prominence can change markedly over time.

Abstract

Unfavorable results of major studies have led to a large shrinkage of the market for hormone replacement therapy (HRT) in the last 6 years. Some scientists continue to strongly support the use of HRT.We analyzed a sample of partisan editorializing articles on HRT to examine their arguments, the reporting of competing interests, the journal venues and their sponsoring societies.Through Thomson ISI database, we selected articles without primary data written by the five most prolific editorialists that addressed clinical topics pertaining to HRT and that were published in regular journal issues in 2002-2008.We recorded the number of articles with a partisan stance and their arguments, the number of partisan articles that reported conflicting interests, and the journal venues and their sponsoring societies publishing the partisan editorials.We analyzed 114 eligible articles (58 editorials, 16 guidelines, 37 reviews, 3 letters), of which 110 (96%) had a partisan stance favoring HRT. Typical arguments were benefits for menopausal and related symptoms (64.9%), criticism of unfavorable studies (78.9%), preclinical data that showed favorable effects of HRT (50%), and benefits for major outcomes such as osteoporosis and fractures (49.1%), cardiovascular disease (31.6%), dementia (24.6%) or colorectal cancer (20.2%), but also even breast cancer (4.4%). All 5 prolific editorialists had financial relationships with hormone manufacturers, but these were reported in only 6 of the 110 partisan articles. Four journals published 15-37 partisan articles each. The medical societies of these journals reported on their websites that several pharmaceutical companies sponsored them or their conferences.There is a considerable body of editorializing articles favoring HRT use and very few of these articles report conflicts of interest. Full disclosure of conflicts of interest is needed, especially for articles without primary data.

Abstract

Clinical proteomics has yielded some early positive results-the identification of potential disease biomarkers-indicating the promise for this analytical approach to improve the current state of the art in clinical practice. However, the inability to verify some candidate molecules in subsequent studies has led to skepticism among many clinicians and regulatory bodies, and it has become evident that commonly encountered shortcomings in fundamental aspects of experimental design mainly during biomarker discovery must be addressed in order to provide robust data. In this Perspective, we assert that successful studies generally use suitable statistical approaches for biomarker definition and confirm results in independent test sets; in addition, we describe a brief set of practical and feasible recommendations that we have developed for investigators to properly identify and qualify proteomic biomarkers, which could also be used as reporting requirements. Such recommendations should help put proteomic biomarker discovery on the solid ground needed for turning the old promise into a new reality.

Abstract

Mounting evidence suggests that there is frequently considerable variation in the risk of the outcome of interest in clinical trial populations. These differences in risk will often cause clinically important heterogeneity in treatment effects (HTE) across the trial population, such that the balance between treatment risks and benefits may differ substantially between large identifiable patient subgroups; the "average" benefit observed in the summary result may even be non-representative of the treatment effect for a typical patient in the trial. Conventional subgroup analyses, which examine whether specific patient characteristics modify the effects of treatment, are usually unable to detect even large variations in treatment benefit (and harm) across risk groups because they do not account for the fact that patients have multiple characteristics simultaneously that affect the likelihood of treatment benefit. Based upon recent evidence on optimal statistical approaches to assessing HTE, we propose a framework that prioritizes the analysis and reporting of multivariate risk-based HTE and suggests that other subgroup analyses should be explicitly labeled either as primary subgroup analyses (well-motivated by prior evidence and intended to produce clinically actionable results) or secondary (exploratory) subgroup analyses (performed to inform future research). A standardized and transparent approach to HTE assessment and reporting could substantially improve clinical trial utility and interpretability.

Abstract

To estimate the comparative effectiveness of medical interventions in adults versus children.We identified from the Cochrane Database of Systematic Reviews (Issue 1, 2007) meta-analyses with data on at least 1 adult and 1 pediatric randomized trial with binary primary efficacy outcome. For each meta-analysis, we calculated the summary odds ratio of the adult trials and the pediatric trials, respectively; the relative odds ratio (ROR) of the adult versus pediatric odds ratios per meta-analysis; and the summary ROR across all meta-analyses. ROR <1 means that the experimental intervention is more unfavorable in children than adults.Across 128 eligible meta-analyses (1051 adult and 343 pediatric trials), the summary ROR did not show a statistically significant difference between adults and children (0.96; 95% confidence intervals, 0.86 to 1.08). However, in all meta-analyses except for 1, the individual ROR's 95% confidence intervals could not exclude a relative difference in efficacy over 20%. In two-thirds, the relative difference in observed point estimates exceeded 50%. Nine statistically significant discrepancies were identified; 4 of them were also clinically important.Treatment effects are on average similar in adults and children, but available evidence leaves large uncertainty about their relative efficacy. Clinically important discrepancies may occur.

Abstract

Genome-wide association studies (GWASs) have created a paradigm shift in discovering genetic associations for common diseases and phenotypes, but it is unclear whether the thousands of candidate genetic association studies performed in the pre-GWAS era had found any reliable associations for common diseases and phenotypes. We aimed to systematically evaluate whether loci proposed to harbor candidate associations before the advent of GWASs are replicated in GWASs. The GWAS data published through August, 2008 and included in the NHGRI catalog were screened and variants in candidate loci were selected on the basis of statistical significance (P<0.05) to create a list of independent, non-redundant associations. Altogether, 159 articles on GWASs were evaluated, 100 of which addressed past proposed candidate loci. A total of 291 independent, nominally significant (P<0.05) candidate gene associations were assembled after keeping only the SNP with lowest P-value for each locus and each phenotype; 108 of those had P<10(-3) for association and 41 had P<10(-7). A total of 22 of these 41 candidate gene associations pertained to binary phenotypes with a median odds ratio=2.91 (IQR: 1.82-4.6) and median minor allele frequency=0.17 (IQR: 0.12-0.29) in Caucasians; for comparison, 60 new associations of binary outcomes with P<10(-7) discovered in the same GWASs had much smaller effects (median odds ratio 1.30, IQR: 1.18-1.58) and modestly larger minor allele frequencies (median 0.27, IQR: 0.15-0.43). Overall, few of the numerous genetic associations proposed in the candidate gene era have been replicated in GWASs, but those that have been conclusively replicated have large genetic effects that should not be discarded.

Abstract

Since 2007, genome-wide association (GWA) studies have identified numerous well-supported, novel genetic risk loci for common cancers; however, there are concerns that this technology is reaching its limits. We provide an overview of GWA-identified genetic associations with solid tumors. We simulated the distribution of population risk alleles for colorectal, prostate, testicular, and thyroid cancers based on genetic variants identified in GWA studies. We also evaluated whether statistical power to detect typical genetic effects could be improved with studies performing GWA analyses of all available samples rather than multistage designs. Fifty-six eligible articles yielded 92 eligible associations between cancer phenotypes and genetic variants with a median per-allele odds ratio (OR) of 1.22 (interquartile range = 1.15-1.36). Half of the associations pertained to prostate, colorectal, or breast cancer. Individuals at the upper quartile of simulated risk had only 2.1- to 4.2-fold higher relative risk than those in the lower quartile. Comprehensive evaluation of currently available samples with GWA platforms would yield few additional variants with per-allele OR = 1.4, but many more variants with OR = 1.2 could be detected; statistical power to detect weak associations (OR = 1.07) would still be negligible. The GWA approach is effective in identifying common genetic variants with moderate effect; however, identifying loci with very small effects and rare variants will require major new efforts. At present, the utility of GWA-identified risk loci in risk stratification for cancer is limited.

Abstract

Genome-wide association studies have found type 2 diabetes-associated variants in the HNF1B gene to exhibit reciprocal associations with prostate cancer risk. We aimed to identify whether these variants may have an effect on cancer risk in general versus a specific effect on prostate cancer only.In a collaborative analysis, we collected data from GWAS of cancer phenotypes for the frequently reported variants of HNF1B, rs4430796 and rs7501939, which are in linkage disequilibrium (r(2) = 0.76, HapMap CEU). Overall, the analysis included 16 datasets on rs4430796 with 19,640 cancer cases and 21,929 controls; and 21 datasets on rs7501939 with 26,923 cases and 49,085 controls. Malignancies other than prostate cancer included colorectal, breast, lung and pancreatic cancers, and melanoma. Meta-analysis showed large between-dataset heterogeneity that was driven by different effects in prostate cancer and other cancers. The per-T2D-risk-allele odds ratios (95% confidence intervals) for rs4430796 were 0.79 (0.76, 0.83)] per G allele for prostate cancer (p<10(-15) for both); and 1.03 (0.99, 1.07) for all other cancers. Similarly for rs7501939 the per-T2D-risk-allele odds ratios (95% confidence intervals) were 0.80 (0.77, 0.83) per T allele for prostate cancer (p<10(-15) for both); and 1.00 (0.97, 1.04) for all other cancers. No malignancy other than prostate cancer had a nominally statistically significant association.The examined HNF1B variants have a highly specific effect on prostate cancer risk with no apparent association with any of the other studied cancer types.

Abstract

Images are important for conveying information, but there is no empirical evidence on whether imaging figures are properly selected and presented in the published medical literature. We therefore evaluated the selection and presentation of radiological imaging figures in major medical journals.We analyzed articles published in 2005 in 12 major general and specialty medical journals that had radiological imaging figures. For each figure, we recorded information on selection, study population, provision of quantitative measurements, color scales and contrast use. Overall, 417 images from 212 articles were analyzed. Any comment/hint on image selection was made in 44 (11%) images (range 0-50% across the 12 journals) and another 37 (9%) (range 0-60%) showed both a normal and abnormal appearance. In 108 images (26%) (range 0-43%) it was unclear whether the image came from the presented study population. Eighty-three images (20%) (range 0-60%) had any quantitative or ordered categorical value on a measure of interest. Information on the distribution of the measure of interest in the study population was given in 59 cases. For 43 images (range 0-40%), a quantitative measurement was provided for the depicted case and the distribution of values in the study population was also available; in those 43 cases there was no over-representation of extreme than average cases (p = 0.37).The selection and presentation of images in the medical literature is often insufficiently documented; quantitative data are sparse and difficult to place in context.

Abstract

Funding is important for scientists' work and may contribute to exceptional research outcomes. We analyzed the funding sources reported in the landmark scientific papers of Nobel Prize winners. Between 2000 and 2008, 70 Nobel laureates won recognition in medicine, physics, and chemistry. Sixty five (70%) of the 93 selected papers related to the Nobel-awarded work reported some funding source including U.S. government sources in 53 (82%), non-U.S. government sources in 19 (29%), and nongovernment sources in 33 (51%). A substantial portion of this exceptional work was unfunded. We contacted Nobel laureates whose landmark papers reported no funding. Thirteen Nobel laureates responded and offered their insights about the funding process and difficulties inherent in funding. Overall, very diverse sources amounting to a total of 64 different listed sponsors supported Nobel-related work. A few public institutions, in particular the U.S. National Institutes of Health (with n=26 funded papers) and the National Science Foundation (with n=17 papers), stood out for their successful record for funding exceptional research. However, Nobel-level work arose even from completely unfunded research, especially when institutions offered a protected environment for dedicated scientists.

Abstract

The educational environment makes an important contribution to student learning. The DREEM questionnaire is a validated tool assessing the environment.To translate and validate the DREEM into Greek.Forward translations from English were produced by three independent Greek translators and then back translations by five independent bilingual translators. The Greek DREEM.v0 that was produced was administered to 831 undergraduate students from six Greek medical schools. Cronbach's alpha and test-retest correlation were used to evaluate reliability and factor analysis was used to assess validity. Questions that increased alpha if deleted and/or sorted unexpectedly in factor analysis were further checked through two focus groups.Questionnaires were returned by 487 respondents (59%), who were representative of all surveyed students by gender but not by year of study or medical school. The instrument's overall alpha was 0.90, and for the learning, teachers, academic, atmosphere and social subscales the alphas were 0.79 (expected 0.69), 0.78 (0.67), 0.69 (0.60), 0.68 (0.69), 0.48 (0.57), respectively. In a subset of the whole sample, test and retest alphas were both 0.90, and mean item scores highly correlated (p<0.001). Factor analysis produced meaningful subscales but not always matching the original ones. Focus group evaluation revealed possible misunderstanding for questions 17, 25, 29 and 38, which were revised in the DREEM.Gr.v1. The group mean overall scale score was 107.7 (SD 20.2), with significant differences across medical schools (p<0.001).Alphas and test-retest correlation suggest the Greek translated and validated DREEM scale is a reliable tool for assessing the medical education environment and for informing policy. Factor analysis and focus group input suggest it is a valid tool. Reasonable school differences suggest the instrument's sensitivity.

Who is afraid of reviewers' comments? Or, why anything can be published and anything can be citedEUROPEAN JOURNAL OF CLINICAL INVESTIGATIONIoannidis, J. P., Tatsioni, A., Karassa, F. B.2010; 40 (4): 285-287

Abstract

Genome-wide association studies have proposed susceptibility variants for rheumatoid arthritis in the TRAF1-C5 locus and 6q23 region. Furthermore, additional independent studies have investigated the same or highly linked polymorphisms in the same regions.To carry out a meta-analysis of the available evidence for the association of polymorphisms in the TRAF1-C5 locus and 6q23 region with rheumatoid arthritis.Data were synthesised for four polymorphisms: rs3761847 (n=13 datasets) and rs2900180 (n=9 datasets) in the TRAF1-C5 locus, and rs10499194 (n=5 datasets) and rs6920220 (n=7 datasets) in the 6q23 region. Meta-analyses for subgroups defined by anti-cyclic citrullinated peptide (anti-CCP) and rheumatoid factor (RF) status were also performed.The polymorphism rs6920220 reached genome-wide statistical significance with p=7.9 x 10(-17) and an allelic odds ratio of 1.24 (95% CI 1.18 to 1.30) and no between-study heterogeneity (I(2)=0%). The risk was significantly stronger in patients with anti-CCP antibodies and in patients with RF. The other three variants showed large between-study heterogeneity across datasets (I(2) range 74-82%); rs10499194 was nominally statistically significant after exclusion of the discovery data. Two variants had genome-wide statistical significance in subgroups defined by the presence of RF (rs3761847 and rs6920220) or anti-CCP (rs6920220).Genetic markers in the 6q23 region and TRAF1-C5 are associated with rheumatoid arthritis, in particular with positive anti-CCP and RF profile. With the exception of rs6920220, which shows highly consistent results, other proposed markers have high between-study heterogeneity that may reflect unrecognised phenotypic or genetic variability (eg, gene environment interactions) within rheumatoid arthritis. Furthermore, these markers may not be the true causative loci but rather be in linkage disequilibrium with the true ones.

Abstract

Genome-wide association studies (GWAS) using population-based designs have identified many genetic loci associated with risk of a range of complex diseases including cancer; however, each locus exerts a very small effect and most heritability remains unexplained. Family-based pedigree studies have also suggested tentative loci linked to increased cancer risk, often characterized by pedigree-specificity. However, comparison between the results of population- and family-based studies shows little concordance. Explanations for this unidentified genetic 'dark matter' of cancer include phenotype ascertainment issues, limited power, gene-gene and gene-environment interactions, population heterogeneity, parent-of-origin-specific effects, and rare and unexplored variants. Many of these reasons converge towards the concept of genetic heterogeneity that might implicate hundreds of genetic variants in regulating cancer risk. Dissecting the dark matter is a challenging task. Further insights can be gained from both population association and pedigree studies.

Abstract

Athletic endurance performance is probably partly under genetic control, but genetic association studies have yielded inconclusive results. The objective of the present study was to evaluate the association of polymorphisms in eight muscle- or metabolism-related genes with endurance performance in participants of the Olympus Marathon running race. We recruited 438 athletes who participated in the 2007 and 2008 annual running events of the Olympus Marathon: a 43.8-km race with an ascent from sea level to 2,690-m altitude and then a descent to 300 m. Phenotypes of interest were the competitive event time at the specific Olympus Marathon where the athlete was enrolled, the fastest reported timing ever achieved in an Olympus Marathon, and how many kilometers per week the athlete ran during the previous year. Eleven polymorphisms in alpha(3)-actinin (ACTN3), AMP deaminase-1 (AMPD1), bradykinin B(2) receptor (BDKRB2), beta(2)-adrenergic receptor (ADRB2), peroxisome proliferator-activated receptor (PPAR)-gamma coactivator-1 alpha (PPARGC1A), PPAR-alpha (PPARA), PPAR-delta (PPARD), and apoliprotein E (APOE) were evaluated. Hardy-Weinberg equilibrium testing on the overall cohort of male athletes showed a significant deviation for BDKRB2 rs1799722 (P = 0.018; P = 0.006 when limited to 316 habitual male runners) with an excess of the TT genotype. Across all athletes, no associations showed nominal statistical significance for any of the three phenotypes, and the same was true when analyses were limited to men (n = 417). When limited to 316 male athletes who identified running as their preferred sport, ADRB2 rs1042713 had nominally significant associations with faster times for the minor (A) allele for the fastest time ever (P = 0.01). The direction of effect was identical as previously postulated only for BDKRB2 rs1799722 and ADRB2 rs1042713, indicating consistency. BDKRB2 rs1799722 and ADRB2 rs1042713 have some support for being implicated in endurance performance among habitual runners and require further investigation.

Abstract

Most clinical trials on medical interventions are sponsored by the industry. The choice of comparators shapes the accumulated evidence. We aimed to assess how often major companies sponsor trials that involve only their own products.Studies were identified by searching ClinicalTrials.gov for trials registered in 2006. We focused on randomized trials involving the 15 companies that had sponsored the largest number of registered trials in ClinicalTrials.gov in that period.Overall, 577 randomized trials were eligible for analysis and 82% had a single industry sponsor [89% (166/187) of the placebo-control trials, 87% (91/105) of trials comparing different doses or ways of administration of the same intervention, and 78% (221/285) of other active control trials]. The compared intervention(s) belonged to a single company in 67% of the trials (89%, 81% and 47% in the three categories respectively). All 15 companies strongly preferred to run trials where they were the only industry sponsor or even the only owner of the assessed interventions. Co-sponsorship typically reflected co-ownership of the same intervention by both companies. Head-to-head comparison of different active interventions developed by different companies occurred in only 18 trials with two or more industry sponsors.Each company generates a clinical research agenda that is strongly focused on its own products, while comparisons involving different interventions from different companies are uncommon. This diminishes the ability to understand the relative merits of different interventions for the same condition.

Abstract

Our goal wasto produce a field synopsis of genetic associations with preterm birth and to set up a publicly available online database summarizing the data.We performed a systematic review and meta-analyses to identify genetic associations with preterm birth. We have set up a publicly available online database of genetic association data on preterm birth called PTBGene (http://ric.einstein.yu.edu/ptbgene/index.html) and report on a structured synopsis thereof as of December 1, 2008.Data on 189 polymorphisms in 84 genes have been included and 36 meta-analyses have been performed. Five gene variants (4 in maternal DNA, one in newborn DNA) have shown nominally significant associations, but all have weak epidemiological credibility.After publishing this field synopsis, the PTBGene database will be regularly updated to keep track of the evolving evidence base of genetic factors in preterm birth with the goal of promoting knowledge sharing and multicenter collaboration among preterm birth research groups.

Abstract

Mutations in the MYOC gene have been shown to explain 5% of unrelated primary open angle glaucoma (POAG) in different populations. In particular, the T377M MYOC mutation has arisen at least three separate times in history, in Great Britain, India, and Greece. The purpose of this study is to investigate the distribution of the mutation among different population groups in the northwestern region of Greece.We explored the distribution of the "Greek" T377M founder mutation in the Epirus region in Northwestern Greece, which could be its origin. Genotyping was performed in POAG cases and controls by PCR amplification of the MYOC gene, followed by digestion with restriction enzyme. Statistical analyses were performed by an exact test, the Kaplan-Meier method and the t-test.In the isolated Chrysovitsa village in the Pindus Mountains, a large POAG family demonstrated the T377M mutation in 20 of 66 family members while no controls from the Epirus region (n = 124) carried this mutation (P < 0.001). Among other POAG cases from Epirus, 2 out of 14 familial cases and 1 out of 80 sporadic cases showed the mutation (P = 0.057). The probability of POAG diagnosis with advancing age among mutation carriers was 23% at age 40, and reached 100% at age 75. POAG patients with the T377M mutation were diagnosed at a mean age of 51 years (SD +/- 13.9), which is younger than the sporadic or familial POAG cases: 63.1 (SD +/- 11) and 66.8 (SD +/- 9.8) years, respectively.The T377M mutation was found in high proportion in members of the Chrysovitsa family (30.3%), in lower proportion in familial POAG cases (14.2%) and seems rare in sporadic POAG cases (1.2%), while no controls (0%) from the Epirus region carried the mutation. Historical and geographical data may explain the distribution of this mutation within Greece and worldwide.

Abstract

Early genome-wide association (GWA) studies on Parkinson's disease (PD) have not been able to yield conclusive, replicable signals of association, perhaps due to limited sample size. We aimed to investigate whether association signals derived from the meta-analysis of the first two GWA investigations might be replicable in different populations. We examined six single-nucleotide polymorphisms (SNPs) (rs1000291, rs1865997, rs2241743, rs2282048, rs2313982, and rs3018626) that had reached nominal significance with at least two of three different strategies proposed in a previous analysis of the original GWA studies. Investigators from the "Genetic Epidemiology of Parkinson's Disease" (GEOPD) consortium were invited to join in this study. Ten teams contributed replication data from 3,458 PD cases and 3,719 controls. The data from the two previously published GWAs (599 PD cases, 592 controls and 443 sibling pairs) were considered as well. All data were synthesized using both fixed and random effects models. The summary allelic odds ratios were ranging from 0.97 to 1.09 by random effects, when all data were included. The summary estimates of the replication data sets (excluding the original GWA data) were very close to 1.00 (range 0.98-1.09) and none of the effects were nominally statistically significant. The replication data sets had significantly different results than the GWA data. Our data do not support evidence that any of these six SNPs reflect susceptibility markers for PD. Much stronger signals of statistical significance in GWA platforms are needed to have substantial chances of replication. Specifically in PD genetics, this would require much larger GWA studies and perhaps novel analytical techniques.

Abstract

Personal genome tests are now offered direct-to-consumer (DTC) via genetic variants identified by genome-wide association studies (GWAS) for common diseases. Tests report risk estimates (age-specific and lifetime) for various diseases based on genotypes at multiple loci. However, uncertainty surrounding such risk estimates has not been systematically investigated. With breast cancer as an example, we examined the combined effect of uncertainties in population incidence rates, genotype frequency, effect sizes, and models of joint effects among genetic variants on lifetime risk estimates. We performed simulations to estimate lifetime breast cancer risk for carriers and noncarriers of genetic variants. We derived population-based cancer incidence rates from Surveillance, Epidemiology, and End Results (SEER) Program and comparative international data. We used data for non-Hispanic white women from 2003 to 2005. We derived genotype frequencies and effect sizes from published GWAS and meta-analyses. For a single genetic variant in FGFR2 gene (rs2981582), combination of uncertainty in these parameters produced risk estimates where upper and lower 95% simulation intervals differed by more than 3-fold. Difference in population incidence rates was the largest contributor to variation in risk estimates. For a panel of five genetic variants, estimated lifetime risk of developing breast cancer before age 80 for a woman that carried all risk variants ranged from 6.1% to 21%, depending on assumptions of additive or multiplicative joint effects and breast cancer incidence rates. Epidemiologic parameters involved in computation of disease risk have substantial uncertainty, and cumulative uncertainty should be properly recognized. Reliance on point estimates alone could be seriously misleading.

Abstract

With heightened interest in predictive medicine, many studies try to document information that can improve prediction of major clinical outcomes.To evaluate the reported design and analysis of studies that examined whether additional predictors improve predictive performance when added to the Framingham risk score (FRS), one of the most widely validated and cited clinical prediction scores.Two independent investigators searched 1908 articles citing the article that described the FRS in 1998 until September 2009 through the ISI Web of Knowledge database. Articles were eligible if they included any analyses comparing the predictive performance of the FRS vs the FRS plus some additional predictor for a prospectively assessed outcome. Data Analyses We recorded information on FRS calculation, modeling of additional predictors, outcomes assessed, population evaluated, subgroup analysis documentation, and flaws in the methods that may have affected the reported improvements in predictive ability. We also evaluated the correlation of reported design and analysis features with the predictive model discrimination and improvements with the additional predictors.We evaluated 79 eligible articles. Forty-nine studies (62%) did not calculate the FRS as it has been proposed, 15 (19%) modeled the additional predictor in more than 1 way and presented only the best-fit or area-under-the-curve (AUC) results for only 1 model, 41 (52%) did not examine the original outcome that the FRS was developed for, 33 (42%) studied a population different from what the FRS was intended for, and 25 (32%) claimed improved prediction in 1 subgroup but only 7 (9%) formally tested subgroup differences. Evaluation of independence in multivariable regressions, discrimination in AUC, calibration, and reclassification were reported in 77, 36, 7, and 7 studies, respectively, but these methods were adequately documented in only 60, 13, 4, and 2 studies, respectively. Overall, 63 studies (80%) claimed some improved prediction. Increase in AUC was larger when the predictive performance of the FRS was lower (rho = -0.57, P < .001). Increase in AUC was significantly larger when evaluation of independence in multivariable regression or discrimination in AUC analysis was not adequately documented and when the additional predictor had been modeled in more than 1 way and only 1 model was reported for AUC.The majority of examined studies claimed that they found factors that could offer additional predictive value beyond what the FRS could achieve; however, most had flaws in their design, analyses, and reporting that cast some doubt on the reliability of the claims for improved prediction.

Abstract

Research standards deviate in genetic versus nongenetic epidemiology. Besides some immutable differences, such as the correlation pattern between variables, these divergent research standards can converge considerably. Current research designs that dissociate genetic and nongenetic measurements are reaching their limits. Studies are needed that massively measure genotypes, nongenetic exposures, and outcomes concurrently.

Abstract

Genetic effects for common variants affecting complex disease risk are subtle. Single genome-wide association (GWA) studies are typically underpowered to detect these effects, and combination of several GWA data sets is needed to enhance discovery. The authors investigated the properties of the discovery process in simulated cumulative meta-analyses of GWA study-derived signals allowing for potential genetic model misspecification and between-study heterogeneity. Variants with null effects on average (but also between-data set heterogeneity) could yield false-positive associations with seemingly homogeneous effects. Random effects had higher than appropriate false-positive rates when there were few data sets. The log-additive model had the lowest false-positive rate. Under heterogeneity, random-effects meta-analyses of 2-10 data sets averaging 1,000 cases/1,000 controls each did not increase power, or the meta-analysis was even less powerful than a single study (power desert). Upward bias in effect estimates and underestimation of between-study heterogeneity were common. Fixed-effects calculations avoided power deserts and maximized discovery of association signals at the expense of much higher false-positive rates. Therefore, random- and fixed-effects models are preferable for different purposes (fixed effects for initial screenings, random effects for generalizability applications). These results may have broader implications for the design and interpretation of large-scale multiteam collaborative studies discovering common gene variants.

Abstract

Osteoarthritis is the most common disease affecting joints in the elderly. We aimed to evaluate if elderly patients are properly represented in clinical trials of diverse osteoarthritis interventions.Clinical trials of osteoarthritis interventions were retrieved from Cochrane Library systematic reviews (2006, issue 2). We examined the age distribution of the trial participants and eligibility criteria.We analyzed data from 219 eligible trials from 18 systematic reviews. The average mean age of the participants was 63 years. Only 13 trials (6.4%) had a mean age between 71 and 80 years and only one trial had a mean age exceeding 80 years. Among trials where the age range of participants was available or could be approximately inferred, we estimated that 66 (38%) trials had not included any patients over 80 years old. Only 23 trials specifically excluded patients over 70 based on reported eligibility criteria, but 168 trials excluded patients with various comorbidities and 142 trials excluded patients receiving other specific treatments.Elderly patients are considerably under-represented in clinical trials of osteoarthritis. This causes an important deficit in the utility, relevance, and generalizability of trial results for this very common condition.

Abstract

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

Abstract

Replication helps ensure that a genotype-phenotype association observed in a genome-wide association (GWA) study represents a credible association and is not a chance finding or an artifact due to uncontrolled biases. We discuss prerequisites for exact replication; issues of heterogeneity; advantages and disadvantages of different methods of data synthesis across multiple studies; frequentist vs. Bayesian inferences for replication; and challenges that arise from multi-team collaborations. While consistent replication can greatly improve the credibility of a genotype-phenotype association, it may not eliminate spurious associations due to biases shared by many studies. Conversely, lack of replication in well-powered follow-up studies usually invalidates the initially proposed association, although occasionally it may point to differences in linkage disequilibrium or effect modifiers across studies.

Abstract

To synthesize the evidence from randomized controlled trials concerning systemic treatment regimens for patients with cancer of unknown primary site (CUP).PubMed and the Cochrane Library Central Registry of Controlled Trials.We retrieved all randomized controlled trials comparing at least two arms of different systemic treatment regimens or a systemic regimen to no treatment in patients with CUP, excluding data on favorable subset CUP, whenever these could be separated. Treatments were categorized according to whether they involved platinum, taxane, both, or neither; non-platinum/non-taxane regimens were also categorized in monotherapy and combination regimens. We extracted or estimated the logarithm of the hazard ratio and its variance for death for each randomized comparison. Multiple-treatments meta-analysis with a hierarchical Bayesian model obtained summary hazard ratios with 95% credibility intervals.Ten articles were eligible for the meta-analysis. No trials compared systemic treatment to best supportive care and all arms referred to chemotherapy regimens. Overall 683 subjects were randomly assigned and eight randomized comparisons were used for the multiple-treatments meta-analysis of survival (543 patients). Multiple-treatments meta-analysis showed no significant benefit for any treatment group over others, with wide credibility intervals. Point estimates of hazard ratios favored platinum, taxane, or both (hazard ratios 0.69, 0.66, and 0.81, respectively, as compared with monotherapy with an agent other than platinum or taxane).No type of chemotherapy has been solidly proven to prolong survival in patients with CUP. Regimens using either platinum or taxanes or both need further testing.

Abstract

Osteoporosis is a highly heritable trait. Many candidate genes have been proposed as being involved in regulating bone mineral density (BMD). Few of these findings have been replicated in independent studies.To assess the relationship between BMD and fracture and all common single-nucleotide polymorphisms (SNPs) in previously proposed osteoporosis candidate genes.Large-scale meta-analysis of genome-wide association data.5 international, multicenter, population-based studies.Data on BMD were obtained from 19 195 participants (14 277 women) from 5 populations of European origin. Data on fracture were obtained from a prospective cohort (n = 5974) from the Netherlands.Systematic literature review using the Human Genome Epidemiology Navigator identified autosomal genes previously evaluated for association with osteoporosis. We explored the common SNPs arising from the haplotype map of the human genome (HapMap) across all these genes. BMD at the femoral neck and lumbar spine was measured by dual-energy x-ray absorptiometry. Fractures were defined as clinically apparent, site-specific, validated nonvertebral and vertebral low-energy fractures.150 candidate genes were identified and 36 016 SNPs in these loci were assessed. SNPs from 9 gene loci (ESR1, LRP4, ITGA1, LRP5, SOST, SPP1, TNFRSF11A, TNFRSF11B, and TNFSF11) were associated with BMD at either site. For most genes, no SNP was statistically significant. For statistically significant SNPs (n = 241), effect sizes ranged from 0.04 to 0.18 SD per allele. SNPs from the LRP5, SOST, SPP1, and TNFRSF11A loci were significantly associated with fracture risk; odds ratios ranged from 1.13 to 1.43 per allele. These effects on fracture were partially independent of BMD at SPP1 and SOST. Limitation: Only common polymorphisms in linkage disequilibrium with SNPs in HapMap could be assessed, and previously reported associations for SNPs in some candidate genes could not be excluded.In this large-scale collaborative genome-wide meta-analysis, 9 of 150 candidate genes were associated with regulation of BMD, 4 of which also significantly affected risk for fracture. However, most candidate genes had no consistent association with BMD.

Abstract

Several trials have addressed whether bifurcation lesions require stenting of both the main vessel and side branch, but uncertainty remains on the benefits of such double versus single stenting of the main vessel only.We have conducted a meta-analysis of randomized trials including patients with coronary bifurcation lesions who were randomly selected to undergo percutaneous coronary intervention by either double or single stenting. Six studies (n=1642 patients) were eligible. There was increased risk of myocardial infarction with double stenting (risk ratio, 1.78; P=0.001 by fixed effects; risk ratio, 1.49 with Bayesian meta-analysis). The summary point estimate suggested also an increased risk of stent thrombosis with double stenting, but the difference was not nominally significant given the sparse data (risk ratio, 1.85; P=0.19). No obvious difference was seen for death (risk ratio, 0.81; P=0.66) and target lesion revascularization (risk ratio, 1.09; P=0.67).Stenting of both the main vessel and side branch in bifurcation lesions may increase myocardial infarction and stent thrombosis risk compared with stenting of the main vessel only.

Abstract

Systematic reviews and meta-analyses are essential to summarize evidence relating to efficacy and safety of health care interventions accurately and reliably. The clarity and transparency of these reports, however, is not optimal. Poor reporting of systematic reviews diminishes their value to clinicians, policy makers, and other users. Since the development of the QUOROM (QUality Of Reporting Of Meta-analysis) Statement--a reporting guideline published in 1999--there have been several conceptual, methodological, and practical advances regarding the conduct and reporting of systematic reviews and meta-analyses. Also, reviews of published systematic reviews have found that key information about these studies is often poorly reported. Realizing these issues, an international group that included experienced authors and methodologists developed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) as an evolution of the original QUOROM guideline for systematic reviews and meta-analyses of evaluations of health care interventions. The PRISMA Statement consists of a 27-item checklist and a four-phase flow diagram. The checklist includes items deemed essential for transparent reporting of a systematic review. In this Explanation and Elaboration document, we explain the meaning and rationale for each checklist item. For each item, we include an example of good reporting and, where possible, references to relevant empirical studies and methodological literature. The PRISMA Statement, this document, and the associated Web site (http://www.prisma-statement.org/) should be helpful resources to improve reporting of systematic reviews and meta-analyses.

Abstract

For most associations of common single nucleotide polymorphisms (SNPs) with common diseases, the genetic model of inheritance is unknown. The authors extended and applied a Bayesian meta-analysis approach to data from 19 studies on 17 replicated associations with type 2 diabetes. For 13 SNPs, the data fitted very well to an additive model of inheritance for the diabetes risk allele; for 4 SNPs, the data were consistent with either an additive model or a dominant model; and for 2 SNPs, the data were consistent with an additive or recessive model. Results were robust to the use of different priors and after exclusion of data for which index SNPs had been examined indirectly through proxy markers. The Bayesian meta-analysis model yielded point estimates for the genetic effects that were very similar to those previously reported based on fixed- or random-effects models, but uncertainty about several of the effects was substantially larger. The authors also examined the extent of between-study heterogeneity in the genetic model and found generally small between-study deviation values for the genetic model parameter. Heterosis could not be excluded for 4 SNPs. Information on the genetic model of robustly replicated association signals derived from genome-wide association studies may be useful for predictive modeling and for designing biologic and functional experiments.

Abstract

We aimed to evaluate the prognostic significance of traditional clinical predictors in osteosarcoma through an international collaboration of 10 teams of investigators (2680 patients) who participated. In multivariate models the mortality risk increased with older age, presence of metastatic disease at diagnosis, development of local recurrence when the patient was first seen, use of amputation instead of limb salvage/wide resection, employment of unusual treatments, use of chemotherapeutic regimens other than anthracycline and platinum and use of methotrexate. It was also influenced by the site of the tumour. The risk of metastasis increased when metastatic disease was present at the time the patient was first seen and also increased with use of amputation or unusual treatment combinations or chemotherapy regimens not including anthracycline and platinum. Local recurrence risk was higher in older patients, in those who had local recurrence when first seen and when no anthracycline and platinum were used in chemotherapy. Results were similar when limited to patients seen after 1990 and treated with surgery plus combination chemotherapy. This large-scale international collaboration identifies strong predictors of major clinical outcomes in osteosarcoma.

Abstract

Systematic reviews and meta-analyses are essential to summarize evidence relating to efficacy and safety of health care interventions accurately and reliably. The clarity and transparency of these reports, however, is not optimal. Poor reporting of systematic reviews diminishes their value to clinicians, policy makers, and other users. Since the development of the QUOROM (QUality Of Reporting Of Meta-analysis) Statement-a reporting guideline published in 1999-there have been several conceptual, methodological, and practical advances regarding the conduct and reporting of systematic reviews and meta-analyses. Also, reviews of published systematic reviews have found that key information about these studies is often poorly reported. Realizing these issues, an international group that included experienced authors and methodologists developed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) as an evolution of the original QUOROM guideline for systematic reviews and meta-analyses of evaluations of health care interventions. The PRISMA Statement consists of a 27-item checklist and a four-phase flow diagram. The checklist includes items deemed essential for transparent reporting of a systematic review. In this Explanation and Elaboration document, we explain the meaning and rationale for each checklist item. For each item, we include an example of good reporting and, where possible, references to relevant empirical studies and methodological literature. The PRISMA Statement, this document, and the associated Web site (www.prisma-statement.org) should be helpful resources to improve reporting of systematic reviews and meta-analyses.

Abstract

The increasing availability of personal genomic tests has led to discussions about the validity and utility of such tests and the balance of benefits and harms. A multidisciplinary workshop was convened by the National Institutes of Health and the Centers for Disease Control and Prevention to review the scientific foundation for using personal genomics in risk assessment and disease prevention and to develop recommendations for targeted research. The clinical validity and utility of personal genomics is a moving target with rapidly developing discoveries but little translation research to close the gap between discoveries and health impact. Workshop participants made recommendations in five domains: (1) developing and applying scientific standards for assessing personal genomic tests; (2) developing and applying a multidisciplinary research agenda, including observational studies and clinical trials to fill knowledge gaps in clinical validity and utility; (3) enhancing credible knowledge synthesis and information dissemination to clinicians and consumers; (4) linking scientific findings to evidence-based recommendations for use of personal genomics; and (5) assessing how the concept of personal utility can affect health benefits, costs, and risks by developing appropriate metrics for evaluation. To fulfill the promise of personal genomics, a rigorous multidisciplinary research agenda is needed.

Abstract

Genome-wide association studies (GWAS) have led to a rapid increase in available data on common genetic variants and phenotypes and numerous discoveries of new loci associated with susceptibility to common complex diseases. Integrating the evidence from GWAS and candidate gene studies depends on concerted efforts in data production, online publication, database development, and continuously updated data synthesis. Here the authors summarize current experience and challenges on these fronts, which were discussed at a 2008 multidisciplinary workshop sponsored by the Human Genome Epidemiology Network. Comprehensive field synopses that integrate many reported gene-disease associations have been systematically developed for several fields, including Alzheimer's disease, schizophrenia, bladder cancer, coronary heart disease, preterm birth, and DNA repair genes in various cancers. The authors summarize insights from these field synopses and discuss remaining unresolved issues -- especially in the light of evidence from GWAS, for which they summarize empirical P-value and effect-size data on 223 discovered associations for binary outcomes (142 with P < 10(-7)). They also present a vision of collaboration that builds reliable cumulative evidence for genetic associations with common complex diseases and a transparent, distributed, authoritative knowledge base on genetic variation and human health. As a next step in the evolution of Human Genome Epidemiology reviews, the authors invite investigators to submit field synopses for possible publication in the American Journal of Epidemiology.

Abstract

Influenza H5N1 is thought to be a likely causative agent for a future human influenza pandemic. Several types of H5N1 vaccine have been tested, including different doses and adjuvants, and a meta-analysis is needed to identify the best formulation. We searched Medline, Embase, the Cochrane Library, and other online databases to February, 2009, in any language for randomised trials comparing different H5N1 vaccines with or without placebo in healthy adults. Primary outcomes were seroconversion, seroresponse, or both according to haemagglutination-inhibition and microneutralisation. Secondary outcomes were adverse events. Because of the large number of compared formulations, multiple-treatments meta-analysis was used for primary outcomes. Direct-comparison meta-analyses were also done. We included 13 trials, which assessed 58 groups. With non-aluminium adjuvant, sufficiently high immunogenicity (greater than 70%) was achieved even at 12 microg or less (given as two doses of 6 microg or less), and higher doses did not provide major improvements. Immunogenicity for non-adjuvanted and aluminium-adjuvanted formulations increased with increasing dose, but was not sufficiently high. No serious vaccine-related adverse events were reported across 9600 participants. Currently, H5N1 influenza vaccines with non-aluminium adjuvants might represent the best available option in a pandemic. Large-scale studies are needed to verify the high immunogenicity of non-aluminium-adjuvanted vaccines that use very low doses of antigen.

Abstract

Systematic reviews and meta-analyses are essential to summarise evidence relating to efficacy and safety of healthcare interventions accurately and reliably. The clarity and transparency of these reports, however, are not optimal. Poor reporting of systematic reviews diminishes their value to clinicians, policy makers, and other users. Since the development of the QUOROM (quality of reporting of meta-analysis) statement-a reporting guideline published in 1999-there have been several conceptual, methodological, and practical advances regarding the conduct and reporting of systematic reviews and meta-analyses. Also, reviews of published systematic reviews have found that key information about these studies is often poorly reported. Realising these issues, an international group that included experienced authors and methodologists developed PRISMA (preferred reporting items for systematic reviews and meta-analyses) as an evolution of the original QUOROM guideline for systematic reviews and meta-analyses of evaluations of health care interventions. The PRISMA statement consists of a 27-item checklist and a four-phase flow diagram. The checklist includes items deemed essential for transparent reporting of a systematic review. In this explanation and elaboration document, we explain the meaning and rationale for each checklist item. For each item, we include an example of good reporting and, where possible, references to relevant empirical studies and methodological literature. The PRISMA statement, this document, and the associated website (www.prisma-statement.org/) should be helpful resources to improve reporting of systematic reviews and meta-analyses.

Abstract

Systematic reviews and meta-analyses are essential to summarize evidence relating to efficacy and safety of health care interventions accurately and reliably. The clarity and transparency of these reports, however, is not optimal. Poor reporting of systematic reviews diminishes their value to clinicians, policy makers, and other users.Since the development of the QUOROM (QUality Of Reporting Of Meta-analysis) Statement--a reporting guideline published in 1999--there have been several conceptual, methodological, and practical advances regarding the conduct and reporting of systematic reviews and meta-analyses. Also, reviews of published systematic reviews have found that key information about these studies is often poorly reported. Realizing these issues, an international group that included experienced authors and methodologists developed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) as an evolution of the original QUOROM guideline for systematic reviews and meta-analyses of evaluations of health care interventions.The PRISMA Statement consists of a 27-item checklist and a four-phase flow diagram. The checklist includes items deemed essential for transparent reporting of a systematic review. In this Explanation and Elaboration document, we explain the meaning and rationale for each checklist item. For each item, we include an example of good reporting and, where possible, references to relevant empirical studies and methodological literature. The PRISMA Statement, this document, and the associated Web site (http://www.prisma-statement.org/) should be helpful resources to improve reporting of systematic reviews and meta-analyses.

Abstract

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence, the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association (STREGA) studies initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed, but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

Abstract

GDF5 and FRZB have been proposed as genetic loci conferring susceptibility to osteoarthritis (OA); however, the results of several studies investigating the association of OA with the rs143383 polymorphism of the GDF5 gene or the rs7775 and rs288326 polymorphisms of the FRZB gene have been conflicting or inconclusive. To examine these associations, we performed a large-scale meta-analysis of individual-level data.Fourteen teams contributed data on polymorphisms and knee, hip, and hand OA. For rs143383, the total number of cases and controls, respectively, was 5,789 and 7,850 for hip OA, 5,085 and 8,135 for knee OA, and 4,040 and 4,792 for hand OA. For rs7775, the respective sample sizes were 4,352 and 10,843 for hip OA, 3,545 and 6,085 for knee OA, and 4,010 and 5,151 for hand OA, and for rs288326, they were 4,346 and 8,034 for hip OA, 3,595 and 6,106 for knee OA, and 3,982 and 5,152 for hand OA. For each individual study, sex-specific odds ratios (ORs) were calculated for each OA phenotype that had been investigated. The ORs for each phenotype were synthesized using both fixed-effects and random-effects models for allele-based effects, and also for haplotype effects for FRZB.A significant random-effects summary OR for knee OA was demonstrated for rs143383 (1.15 [95% confidence interval 1.09-1.22]) (P=9.4x10(-7)), with no significant between-study heterogeneity. Estimates of effect sizes for hip and hand OA were similar, but a large between-study heterogeneity was observed, and statistical significance was borderline (for OA of the hip [P=0.016]) or absent (for OA of the hand [P=0.19]). Analyses for FRZB polymorphisms and haplotypes did not reveal any statistically significant signals, except for a borderline association of rs288326 with hip OA (P=0.019).Evidence of an association between the GDF5 rs143383 polymorphism and OA is substantially strong, but the genetic effects are consistent across different populations only for knee OA. Findings of this collaborative analysis do not support the notion that FRZB rs7775 or rs288326 has any sizable genetic effect on OA phenotypes.

Abstract

Studies using genome-wide platforms have yielded an unprecedented number of promising signals of association between genomic variants and human traits. This Review addresses the steps required to validate, augment and refine such signals to identify underlying causal variants for well-defined phenotypes. These steps include: large-scale exact replication across both similar and diverse populations; fine mapping and resequencing; determination of the most informative markers and multiple independent informative loci; incorporation of functional information; and improved phenotype mapping of the implicated genetic effects. Even in cases for which replication proves that an effect exists, confident localization of the causal variant often remains elusive.

Abstract

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed, but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct or analysis.

Abstract

To assess available evidence on the use of end-points (outcome measures) in clinical trials in systemic lupus erythematosus (SLE), as a part of the development of evidence-based recommendations for points to consider in clinical trials in SLE.The European League Against Rheumatism (EULAR) Task Force on SLE comprised 19 specialists, a clinical epidemiologist and a research fellow. Key questions addressing the evidence for clinical trial end-points in SLE were compiled using the Delphi technique. A systematic search of the PubMed and Cochrane Library databases was performed using McMaster/Hedges clinical query strategies and an array of relevant terms. Evidence was categorised based on sample size and type of design, and the categories of available evidence were identified for each recommendation. The strength of recommendation was assessed based on the category of available evidence and agreement on the statements was measured across the 19 specialists.Eight questions were generated regarding end-points for clinical trials. The evidence to support each proposition was evaluated. The literature review revealed that most outcome measures used in phase 2/3 trials in SLE have not been formally validated in clinical trials, although some indirect validation has been undertaken.This systematic literature review forms the evidence base considered in the development of the EULAR recommendations for end-points in clinical trials in SLE.

Abstract

Systemic lupus erythematosus (SLE) is a complex multi-organ disease, characterised by relapses and remissions.ng a high-quality randomised controlled trial poses many challenges. We have developed evidenced-based recommendations for points to consider in conducting clinical trials in patients with SLE.The EULAR Task Force on SLE comprised 19 specialists and a clinical epidemiologist. Initially, the evidence for clinical trial end-points in SLE was evaluated and this has been reported separately. A consensus approach was developed by the SLE Task Force in formulating recommendations for points to consider when conducting clinical trials in SLE.The literature review revealed that most outcome measures used in phase 2/3 trials in SLE have not actually been validated in clinical trials, although other forms of validation have been undertaken. The final recommendations for points to consider for conducting clinical trials in SLE address the following areas: study design, eligibility criteria, outcome measures including adverse events, concomitant therapies for SLE and its complications.Recommendations for points to consider when conducting clinical trials in SLE were developed using an evidence-based approach followed by expert consensus. The recommendations should be disseminated, implemented and then reviewed in detail and revised using an evidence-based approach in about 5 years, by which time there will be further evidence to consider from current clinical trials.

Abstract

We evaluated whether articles on molecular diagnostic tests interpret appropriately the clinical applicability of their results.We selected original-research articles published in 2006 that addressed the diagnostic value of a molecular test. We defined overinterpretation of clinical applicability by means of prespecified rules that evaluated study design, conclusions regarding applicability, presence of statements suggesting the need for further clinical evaluation of the test, and diagnostic accuracy. Two reviewers independently evaluated the articles; consensus was reached after discussion and arbitration by a third reviewer.Of 108 articles included in the study, 82 (76%) used a design that used healthy controls or alternative-diagnosis controls, only 15 (11%) addressed a clinically relevant population similar to that in which the test might be applied in practice, 104 articles (96%) made definitely favorable or promising statements regarding clinical applicability, and 61 (56%) of the articles apparently overinterpreted the clinical applicability of their findings. Articles published in journals with higher impact factors were more likely to overinterpret their results than those with lower impact factors (adjusted odds ratio, 1.71 per impact factor quartile; 95% CI, 1.09-2.69; P = 0.020). Overinterpretation was more common when authors were based in laboratories than in clinical settings (adjusted odds ratio, 18.7; 95% CI, 1.41-249; P = 0.036).Although expectations are high for new diagnostic tests based on molecular techniques, the majority of published research has involved preclinical phases of research. Overinterpretation of the clinical applicability of findings for new molecular diagnostic tests is common.

Abstract

Two large trials, Clinical Outcomes Utilizing Revascularization and Aggressive Drug Evaluation (COURAGE) and Occluded Artery Trial (OAT), found no benefits of percutaneous coronary intervention (PCI) versus optimal medical therapy in chronic stable coronary artery disease and chronic total occlusion.We examined the stance of articles citing COURAGE and OAT to determine whether some authors continue to defend PCI despite this evidence, what persisting counterarguments are raised to express reservations, and whether specific characteristics of the citations are associated with reservations. We evaluated all citing articles entered in the Web of Science until February 1, 2008. Specific characteristics were recorded for each eligible citation, and a citation content analysis was performed. Counterarguments were categorized on participants, interventions, comparisons, and outcomes.Of 54 articles citing COURAGE and 33 articles citing OAT, 10 (19%) and 5 (15%), respectively, had an overall reserved stance. Alluded reservations included lack of power, eroded effects from crossover, selective inclusion and exclusion of specific types of patients, suboptimal clinical setting, use of bare-metal stents, suspiciously good results in the conservative treatment arm, and suboptimal outcome choices or definitions. Reserved articles were more likely than unreserved ones to have an interventional cardiologist as corresponding author (odds ratio 5.2, 95% confidence interval 1.6-17.1; P = .007) and to be commentaries focusing on one of these trials (odds ratio 3.3, 95% confidence interval 1.0-11.0; P = .05).Despite strong randomized evidence, a fraction of the literature, mostly corresponded by interventional cardiologists, continues to raise reservations about recently contradicted indications of PCI.

Abstract

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

Abstract

Consortia of investigators currently compile sufficiently large sample sizes to investigate the effects of low-risk susceptibility genetic variants. It is not clear how the results obtained by consortia compare with those derived from meta-analyses of published studies.We performed meta-analyses of published data for 16 genetic polymorphisms investigated by the Breast Cancer Association Consortium, and compared sample sizes, heterogeneity, and effect sizes. PubMed, Web of Science, and Human Genome Epidemiology Network databases were searched for breast cancer case-control association studies.We found that meta-analyses of published data and consortium analyses were based on substantially different data. Published data by non-consortium teams amounted on average to 26.9% of all available data (range 3.0 -50.0%). Both approaches showed statistically significant decreased breast cancer risks for CASP8 D302H. The meta-analyses of published data demonstrated statistically significant results for five other genes and the consortium analyses for two other genes, but the strength of this evidence, evaluated on the basis of the Venice criteria, was not strong.Because both approaches identified the same gene out of 16 candidates, the methods can be complimentary. The expense and complexity of consortium-based studies should be considered vis-à-vis the potential methodological limitations of synthesis of published studies.

Abstract

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information into the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and issues of data volume that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

Abstract

Meta-analyses of observational studies often get spuriously precise results. We aimed to factor this skepticism in meta-analysis calculations.We developed a simple sensitivity analysis starting from the assumption that any single observational study cannot give us more than a maximum certainty c% (called credibility ceiling) that an effect is in a particular direction and not in the other. Each study included in meta-analysis is adjusted for different credibility ceilings c and the consistency of the conclusion examined. We applied the method in three meta-analyses of observational studies with nominally statistically significant summary effects (mortality with teaching versus nonteaching health care; risk of non-Hodgkin's lymphoma with hair dyes; mortality with omega-3 fatty acids).Between-study heterogeneity I(2) estimates dropped from 36%-72% without a ceiling effect to 0% with ceilings of 9%, 4%, and 4% in the three meta-analyses, respectively. Nominal statistical significance was lost with ceilings of 10%, 8%, and 11%, respectively. The likelihood ratios suggested that even with minimal ceiling effects, there was no strong support for the credibility of each of these three associations.Consideration of credibility ceilings allows conservative interpretation of observational evidence and can be applied routinely to meta-analyses of observational studies.

Abstract

Given the complexity of microarray-based gene expression studies, guidelines encourage transparent design and public data availability. Several journals require public data deposition and several public databases exist. However, not all data are publicly available, and even when available, it is unknown whether the published results are reproducible by independent scientists. Here we evaluated the replication of data analyses in 18 articles on microarray-based gene expression profiling published in Nature Genetics in 2005-2006. One table or figure from each article was independently evaluated by two teams of analysts. We reproduced two analyses in principle and six partially or with some discrepancies; ten could not be reproduced. The main reason for failure to reproduce was data unavailability, and discrepancies were mostly due to incomplete data annotation or specification of data processing and analysis. Repeatability of published microarray studies is apparently limited. More strict publication rules enforcing public data availability and explicit description of data processing and analysis should be considered.

Abstract

Genome-wide association (GWA) platforms have yielded a rapidly increasing number of new genetic markers. The ability of these markers to improve prediction of clinically important outcomes is debated.A systematic review was performed of GWA-derived markers associated with cardiovascular outcomes or other phenotypes that represent common established risk factors for cardiovascular outcomes. Sources of information included the National Human Genome Research Institute catalog of published GWA studies, and perusal of the eligible GWA articles, meta-analyses on the respective associations, and articles on the incremental predictive performance of common variants in the GWA era. A total of 95 eligible associations were retrieved from the National Human Genome Research Institute catalogue of published GWA studies as of September 2008. Of those 36 have statistical support of P<10(-7). In depth evaluation of the respective articles shows 28 independent associations with such statistical support, pertaining to coronary artery disease, myocardial infarction, atrial fibrillation/flutter, prolongation of QT interval, as well as type 2 diabetes, body mass index, high-density lipoprotein levels, low-density lipoprotein levels, and nicotine dependence. Between-study heterogeneity is not taken into account usually, but it seems common and it would pose a challenge to generalizability across different populations for these markers. Still limited data are available in non-white populations. Effect sizes are small and may be even smaller in subsequent replications and meta-analysis. Population attributable fractions are substantial, given the large frequency of the risk alleles. However, individualized risk measures are typically very small (proportion of variance explained <1% per marker). When used in conjunction with traditional predictors, improvement in overall prediction (eg, area under the curve) or risk reclassification is limited, and subject to methodological caveats.Despite very promising signals in terms of statistical significance, evidence for improvement in cardiovascular prediction by currently available markers derived from GWA studies is sparse. Clinical use of such markers currently would be premature.

Abstract

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

Abstract

Results from apparently conclusive meta-analyses may be false. A limited number of events from a few small trials and the associated random error may be under-recognized sources of spurious findings. The information size (IS, i.e. number of participants) required for a reliable and conclusive meta-analysis should be no less rigorous than the sample size of a single, optimally powered randomized clinical trial. If a meta-analysis is conducted before a sufficient IS is reached, it should be evaluated in a manner that accounts for the increased risk that the result might represent a chance finding (i.e. applying trial sequential monitoring boundaries).We analysed 33 meta-analyses with a sufficient IS to detect a treatment effect of 15% relative risk reduction (RRR). We successively monitored the results of the meta-analyses by generating interim cumulative meta-analyses after each included trial and evaluated their results using a conventional statistical criterion (alpha = 0.05) and two-sided Lan-DeMets monitoring boundaries. We examined the proportion of false positive results and important inaccuracies in estimates of treatment effects that resulted from the two approaches.Using the random-effects model and final data, 12 of the meta-analyses yielded P > alpha = 0.05, and 21 yielded P = alpha = 0.05. False positive interim results were observed in 3 out of 12 meta-analyses with P > alpha = 0.05. The monitoring boundaries eliminated all false positives. Important inaccuracies in estimates were observed in 6 out of 21 meta-analyses using the conventional P = alpha = 0.05 and 0 out of 21 using the monitoring boundaries.Evaluating statistical inference with trial sequential monitoring boundaries when meta-analyses fall short of a required IS may reduce the risk of false positive results and important inaccurate effect estimates.

Abstract

The advent of genome-wide association studies has allowed considerable progress in the identification and robust replication of common gene variants that confer susceptibility to common diseases and other phenotypes of interest. These genetic effect sizes are almost invariably moderate to small in magnitude and single studies, even if large, are underpowered to detect them with confidence. Meta-analysis of many genome-wide association studies improves the power to detect more associations, and to investigate the consistency or heterogeneity of these associations across diverse datasets and study populations. In this review, we discuss the key methodological issues in the set-up, information gathering and processing, and analysis of meta-analyses of genome-wide association datasets. We illustrate, as an example, the application of meta-analysis methods in the elucidation of common genetic variants associated with Type 2 diabetes. Finally, we discuss the prospects and caveats for future application of meta-analysis methods in the genome-wide setting.

How to Use an Article About Genetic Association C: What Are the Results and Will They Help Me in Caring for My Patients?JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATIONAttia, J., Ioannidis, J. P., Thakkinstian, A., McEvoy, M., Scott, R. J., Minelli, C., Thompson, J., Infante-Rivard, C., Guyatt, G.2009; 301 (3): 304-308

Abstract

In the first 2 articles of this series, we reviewed the basic genetics concepts necessary to understand genetic association studies, and we enumerated the major issues in judging the validity of these studies. In this third article, we review the issues relating to the applicability of the results in the clinical situation. How large and precise are the associations? Many genetic effects are expected to be smaller in magnitude than traditional risk factors. Does the genetic association improve predictive power beyond easily measured clinical variables? In some cases, the additional genetic information adds only a small increment in the predictive ability of a diagnostic or prognostic test. What are the absolute vs relative effects? Even if the genetic risk is high in relative terms, the baseline risk may be very low in absolute terms. Is the risk-associated allele likely to be present in my patient? A risk allele may have a strong effect but be rare in a particular ethnic group. Is the patient likely better off knowing the genetic information? Given that genes cannot be modified, one must weigh whether the genetic information is likely to be helpful in planning other health interventions or initiating behavior change.

Abstract

In the first article of this series, we reviewed the basic genetics concepts necessary to understand genetic association studies. In this second article, we enumerate the major issues in judging the validity of these studies, framed as critical appraisal questions. Was the disease phenotype properly defined and accurately recorded by someone blind to the genetic information? Have any potential differences between disease and nondisease groups, particularly ethnicity, been properly addressed? In genetic studies, one potential cause of spurious associations is differences between cases and controls in ethnicity, a situation termed population stratification. Was measurement of the genetic variants unbiased and accurate? Methods for determining DNA sequence variation are not perfect and may have some measurement error. Do the genotype proportions observe Hardy-Weinberg equilibrium? This simple mathematic rule about the distribution of genetic groups may be one way to check for errors in reading DNA information. Have the investigators adjusted their inferences for multiple comparisons? Given the thousands of genetic markers tested in genome-wide association studies, the potential for false-positive and false-negative results is much higher than in traditional medical studies, and it is particularly important to look for replication of results.

Abstract

To determine how often health surveys and quality of life evaluations reach different conclusions from those of primary efficacy outcomes and whether discordant results make a difference in the interpretation of trial findings.Systematic review.PubMed, contact with authors for missing information, and author survey for unpublished SF-36 data.Randomised trials with SF-36 outcomes (the most extensively validated and used health survey instrument for appraising quality of life) that were published in 2005 in 22 journals with a high impact factor.Analyses on the two composite and eight subdomain SF-36 scores that corresponded to the time and mode of analysis of the primary efficacy outcome.Of 1057 screened trials, 52 were identified as randomised trials with SF-36 results (66 separate comparisons). Only eight trials reported all 10 SF-36 scores in the published articles. For 21 of the 66 comparisons, SF-36 results were discordant for statistical significance compared with the results for primary efficacy outcomes. Of 17 statistically significant SF-36 scores where primary outcomes were not also statistically significant in the same direction, the magnitude of effect was small in six, moderate in six, large in three, and not reported in two. Authors modified the interpretation of study findings based on SF-36 results in only two of the 21 discordant cases. Among 100 additional randomly selected trials not reporting any SF-36 information, at least five had collected SF-36 data but only one had analysed it.SF-36 measurements sometimes produce different results from those of the primary efficacy outcomes but rarely modify the overall interpretation of randomised trials. Quality of life and health related survey information should be utilised more systematically in randomised trials.

Abstract

This is the first in a series of 3 articles serving as an introduction to clinicians wishing to read and critically appraise genetic association studies. We summarize the key concepts in genetics that clinicians must understand to review these studies, including the structure of DNA, transcription and translation, patterns of inheritance, Hardy-Weinberg equilibrium, and linkage disequilibrium. We review the types of DNA variation, including single-nucleotide polymorphisms (SNPs), insertions, and deletions, and how these can affect protein function. We introduce the idea of genetic association for both single-candidate gene and genome-wide association studies, in which thousands of genetic variants are tested for association with disease. We use the APOE polymorphism and its association with dementia as a case study to demonstrate the concepts and introduce the terminology used in this field. The second and third articles will focus on issues of validity and applicability.

Abstract

Several genes encoding for DNA repair molecules implicated in maintaining genomic integrity have been proposed as cancer-susceptibility genes. Although efforts have been made to create synopses for specific fields that summarize the data from genetic association studies, such an overview is not available for genes involved in DNA repair.We have created a regularly updated database of studies addressing associations between DNA repair gene variants (excluding highly penetrant mutations) and different types of cancer. Using 1087 datasets and publicly available data from genome-wide association platforms, meta-analyses using dominant and recessive models were performed on 241 associations between individual variants and specific cancer types that had been tested in two or more independent studies. The epidemiological strength of each association was graded with Venice criteria that assess amount of evidence, replication, and protection from bias. All statistical tests were two-sided.Thirty-one nominally statistically significant (ie, P < .05 without adjustment for multiple comparisons) associations were recorded for 16 genes in dominant and/or recessive model analyses (BRCA2, CCND1, ERCC1, ERCC2, ERCC4, ERCC5, MGMT, NBN, PARP1, POLI, TP53, XPA, XRCC1, XRCC2, XRCC3, and XRCC4). XRCC1, XRCC2, TP53, and ERCC2 variants were each nominally associated with several types of cancer. Three associations were graded as having "strong" credibility, another four had modest credibility, and 24 had weak credibility based on Venice criteria. Requiring more stringent P values to account for multiplicity of comparisons, only the associations of ERCC2 codon 751 (recessive model) and of XRCC1 -77 T>C (dominant model) with lung cancer had P

Abstract

Evidence for medical interventions sometimes derives from data that are no longer up to date. These data can influence the outcomes of meta-analyses, yet do not always reflect current clinical practice. We examined the age of the data used in meta-analyses contained within systematic reviews of medical interventions, and investigated whether authors consider the age of these data in their interpretations.From Issue 4, 2005, of the Cochrane Database of Systematic Reviews we randomly selected 10% of systematic reviews containing at least 1 meta-analysis. From this sample we extracted 1 meta-analysis per primary outcome. We calculated the number of years between the study's publication and 2005 (the year that the systematic review was published), as well as the number of years between the study's publication and the year of the literature search conducted in the study. We assessed whether authors discussed the implications of including less recent data, and, for systematic reviews containing meta-analyses of studies published before 1996, we calculated whether excluding the findings of those studies changed the significance of the outcomes. We repeated these calculations and assessments for 22 systematic reviews containing meta-analyses published in 6 high-impact general medical journals in 2005.For 157 meta-analyses (n = 1149 trials) published in 2005, the median year of the most recent literature search was 2003 (interquartile range [IQR] 2002-04). Two-thirds of these meta-analyses (103/157, 66%) involved no trials published in the preceding 5 years (2001-05). Forty-seven meta-analyses (30%) included no trials published in the preceding 10 years (1996-2005). In another 16 (10%), the statistical significance of the outcomes would have been different had the studies been limited to those published between 1996 and 2005, although in some cases this change in significance would have been due to loss of power. Only 12 (8%) of the meta-analyses discussed the potential implications of including older studies. Among the 22 meta-analyses considered in high-impact general medical journals, 2 included no studies published in the 5 years prior to the reference year (2005), and 18 included at least 1 study published before 1996. Only 4 meta-analyses discussed the implications of including older studies.In most systematic reviews containing meta-analyses of evidence for health care interventions, very recent studies are rare. Researchers who conduct systematic reviews with meta-analyses, and clinicians who read the outcomes of these studies, should be made aware of the potential implications of including less recent data.

Abstract

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

Abstract

Infants born at term by elective caesarean delivery are more likely to develop respiratory morbidity than infants born vaginally. Prophylactic corticosteroids in singleton preterm pregnancies accelerate lung maturation and reduce the incidence of respiratory complications.The objective of this review was to assess the effect of prophylactic corticosteroid administration before elective caesarean section at term, as compared to usual management without corticosteroids, in reducing neonatal respiratory morbidity and admission to special care with respiratory complications.We searched the Cochrane Pregnancy and Chilbirth Group's Trials Register (30 June 2009).Randomised and quasi-randomised controlled trials comparing prophylactic antenatal corticosteroid administration (betamethasone or dexamethasone) with placebo or with no treatment, given before elective caesarean section at term (at or after 37 weeks of gestation).The co-authors assessed the results of the only available trial independently to retrieve data on perinatal outcomes. Results were expressed as risk ratio (RR) or mean differences (MD), together with their 95% confidence intervals (CI).One study comparing prophylactic administration of betamethasone (N = 467) versus usual treatment without steroids (N = 475) in term elective caesarean section was included in the review. Women randomised to treatment group received two intramuscular doses of betamethasone in the 48 hours before delivery, whereas the control group received treatment as usual.Prophylactic betamethasone appeared to significantly decrease the risk of admission to the neonatal intensive care unit for respiratory morbidity (RR 0.15; 95% CI 0.03 to 0.64). However, no statistically significant reduction was found in the incidence of neonatal respiratory distress syndrome (RR 0.32; 95% CI 0.07 to 1.58), transient tachypnoea of the newborn (RR 0.52; 95% CI 0.25 to 1.11), need for mechanical ventilation (RR 4.07; 95% CI 0.46 to 36.27) and length of stay in neonatal intensive care unit (MD) -2.14 days; 95% CI -5.58 to 1.30).There were no reported events of neonatal sepsis, perinatal deaths or maternal trauma infection, therefore results on these outcomes are non-estimable. The study did not provide data on other pre-defined outcomes.The results from the single trial are promising, but more studies with larger samples are needed to investigate the effect of prophylactic steroids in the incidence of neonatal complications per se. Also more data and longer follow up would be needed for potential harms and complications.

Abstract

Many systemic nonhormonal regimens have been evaluated across several hundreds of randomized trials in advanced breast cancer. We aimed to quantify the relative merits of these regimens in prolonging survival.We performed a systematic review of all trials that compared different regimens involving chemotherapy and/or targeted therapy in advanced breast cancer (1973-2007). Regimens were categorized a priori into different treatment types. We performed multiple-treatments meta-analysis and calculated hazard ratios for each treatment category relative to monotherapy with old agents (ie, regimens not including anthracyclines, anthracenediones, vinorelbine, gemcitabine, capecitabine, taxanes, marimastat, thalidomide, trastuzumab, lapatinib, or bevacizumab).We identified 370 eligible randomized trials (54,189 patients), of which 172 (31,552 patients) compared different types of treatment. Survival data from 148 comparisons pertaining to 128 of the 172 trials (26,031 patients, 22 different types of treatment) were available for inclusion in the multiple-treatments meta-analysis. Compared with single-agent chemotherapy with old nonanthracycline drugs, anthracycline regimens achieved 22%-33% relative risk reductions in mortality (ie, hazard ratio [HR] for standard-dose anthracycline-based combination: 0.67, 95% credibility interval [CrI] 0.57-0.78). Several newer regimens achieved further benefits (eg, HR [95% CrI] 0.67 [0.55-0.81] for single-drug taxane, 0.64 [0.53-0.78] for combination of anthracyclines with taxane, 0.49 [0.37-0.67] for taxane-based combination with capecitabine or gemcitabine), and similar benefits were seen with several regimens including molecular targeted treatments. Most regimens had very similar efficacy profiles (<5% difference in HR) as first- and subsequent-line therapies.Stepwise improvements in efficacy of chemotherapy and targeted treatments cumulatively have achieved major improvements in the survival of patients with advanced breast cancer. Many options that can be used in first and subsequent lines of therapy have comparable efficacy profiles.

Abstract

There are considerable expectations about the ability of genome-wide association (GWA) studies to make exciting discoveries about the role of genes in common diseases. GWA studies may allow researchers to identify causal pathways that have not been unveiled before, thus opening new avenues to disease understanding, prevention and therapy. However, there are still many open challenges. One is how to analyse these studies. The problem of false positives and false negatives provides an interesting methodological stimulus to find optimal solutions. Once main genetic effects have been concretely documented, the next question is how to proceed with the investigation of gene-gene and gene-environment interactions. It is possible that what really counts is not the main effect of genes but complex interactions. Finding and interpreting such interactions is not straightforward. Finally, continuous updated integration of all evidence, from both old studies, current GWA investigations and future replication studies, and careful interpretation of the strength of the evidence are crucial to maximize transparency and lead to informative selection of the next steps of research in this field. The present Commentary is a report of an Environmental Cancer Risk, Nutrition and Individual Susceptibility network Workshop held in Venice in October 2007 and discusses some of the problems outlined above, with examples.

Abstract

The authors evaluated whether there is an excess of statistically significant results in studies of genetic associations with Alzheimer's disease reflecting either between-study heterogeneity or bias. Among published articles on genetic associations entered into the comprehensive AlzGene database (www.alzgene.org) through January 31, 2007, 1,348 studies included in 175 meta-analyses with 3 or more studies each were analyzed. The number of observed studies (O) with statistically significant results (P = 0.05 threshold) was compared with the expected number (E) under different assumptions for the magnitude of the effect size. In the main analysis, the plausible effect size of each association was the summary effect presented in the respective meta-analysis. Overall, 19 meta-analyses (all with eventually nonsignificant summary effects) had a documented excess of O over E: Typically single studies had significant effects pointing in opposite directions and early summary effects were dissipated over time. Across the whole domain, O was 235 (17.4%), while E was 164.8 (12.2%) (P < 10(-6)). The excess showed a predilection for meta-analyses with nonsignificant summary effects and between-study heterogeneity. The excess was seen for all levels of statistical significance and also for studies with borderline P values (P = 0.05-0.10). The excess of significant findings may represent significance-chasing biases in a setting of massive testing.

Abstract

While association studies on schizophrenia show conflicting results regarding the importance of the regulator of the G-protein signaling 4 (RGS4) gene, recent work suggests that RGS4 may impact on the structural and functional integrity of the prefrontal cortex. We aimed to study associations of common RGS4 variants with prefrontal dependent cognitive performance and schizotypy endophenotypes at the population level.Four RGS4 single nucleotide polymorphisms (SNP1 [rs10917670], SNP4 [rs951436], SNP7 [rs951439], and SNP18 [rs2661319]) and their haplotypes were selected. Their associations with self-rated schizotypy (SPQ), vigilance, verbal, spatial working memory and antisaccade eye performance were tested with regressions in a representative population of 2,243 young male military conscripts.SNP4 was associated with negative schizotypy (higher SPQ negative factor for common T allele, p = 0.009; p = 0.031 for differences across genotypes) and a similar trend was seen also for common A allele of SNP18 (p = 0.039 for allele-load model; but p = 0.12 for genotype differences). Haplotype analyses showed a similar pattern with a dose-response for the most common haplotype (GGGG) on the negative schizotypy score with or without adjustment for age, IQ and their interaction (p = 0.011 and p = 0.024, respectively). There was no clear evidence for any association of the RGS4 variants with cognitive endophenotypes, except for an isolated effect of SNP18 on antisaccade error rate (p = 0.028 for allele-load model).Common RGS4 variants were associated with negative schizotypal personality traits amongst a large cohort of young healthy individuals. In accordance with recent findings, this may suggest that RGS4 variants impact on the functional integrity of the prefrontal cortex, thus increasing susceptibility for psychotic spectrum disorders.

Abstract

Several approaches are available for evaluating heterogeneity in meta-analysis. Sensitivity analyses are often used, but these are often implemented in various non-standardized ways.We developed and implemented sequential and combinatorial algorithms that evaluate the change in between-study heterogeneity as one or more studies are excluded from the calculations. The algorithms exclude studies aiming to achieve either the maximum or the minimum final I(2) below a desired pre-set threshold. We applied these algorithms in databases of meta-analyses of binary outcome and >/=4 studies from Cochrane Database of Systematic Reviews (Issue 4, 2005, n = 1011) and meta-analyses of genetic associations (n = 50). Two I(2) thresholds were used (50% and 25%).Both algorithms have succeeded in achieving the pre-specified final I(2) thresholds. Differences in the number of excluded studies varied from 0% to 6% depending on the database and the heterogeneity threshold, while it was common to exclude different specific studies. Among meta-analyses with initial I(2) > 50%, in the large majority [19 (90.5%) and 208 (85.9%) in genetic and Cochrane meta-analyses, respectively] exclusion of one or two studies sufficed to decrease I(2) < 50%. Similarly, among meta-analyses with initial I(2) > 25%, in most cases [16 (57.1%) and 382 (81.3%), respectively) exclusion of one or two studies sufficed to decrease heterogeneity even <25%. The number of excluded studies correlated modestly with initial estimated I(2) (correlation coefficients 0.52-0.68 depending on algorithm used).The proposed algorithms can be routinely applied in meta-analyses as standardized sensitivity analyses for heterogeneity. Caution is needed evaluating post hoc which specific studies are responsible for the heterogeneity.

Interpretation of tests of heterogeneity and bias in meta-analysisJOURNAL OF EVALUATION IN CLINICAL PRACTICEIoannidis, J. P.2008; 14 (5): 951-957

Abstract

Statistical tests of heterogeneity and bias, in particular publication bias, are very popular in meta-analyses. These tests use statistical approaches whose limitations are often not recognized. Moreover, it is often implied with inappropriate confidence that these tests can provide reliable answers to questions that in essence are not of statistical nature. Statistical heterogeneity is only a correlate of clinical and pragmatic heterogeneity and the correlation may sometimes be weak. Similarly, statistical signals may hint to bias, but seen in isolation they cannot fully prove or disprove bias in general, let alone specific causes of bias, such as publication bias in particular. Both false-positive and false-negative signals of heterogeneity and bias can be common and their prevalence may be anticipated based on some rational considerations. Here I discuss the major common challenges and flaws that emerge in using and interpreting statistical tests of heterogeneity and bias in meta-analyses. I discuss misinterpretations that can occur at the level of statistical inference, clinical/pragmatic inference and specific cause attribution. Suggestions are made on how to avoid these flaws, use these tests properly and learn from them.

Abstract

Genome-wide testing platforms are increasingly used to promote "agnostic" approaches to the discovery of gene variants associated with the risk of many common diseases and quantitative traits. The early track record of genome-wide association (GWA) studies suggests that some proposed associations are replicated quite consistently with large-scale subsequent evidence from multiple studies, others have a more inconsistent replication record, some have failed to be replicated by independent investigators and many more early proposed associations await further replication. An important question is how to calibrate the credibility of these postulated associations. A simple Bayesian method is applied here to achieve such calibration. The variability of the estimated credibility is examined under different assumptions. Empirical examples are drawn from existing GWA studies. It is demonstrated that the credibility of different proposed associations can cover a very wide range. The credibility of specific associations usually remains relatively robust when different plausible assumptions are made (within a reasonable range) for the prior odds of an association being true, or the magnitude of the anticipated effect size for genetic associations. Heterogeneity and bias assumptions can have a more major impact on the credibility estimates and thus they need very careful consideration in each case. Credibility calibration may be used in conjunction with qualitative criteria for the appraisal of the cumulative evidence that take into consideration the amount, consistency, and protection from bias in the data.

Abstract

Systemic sclerosis is a rare and potentially devastating connective tissue disease. It is highly heterogeneous in terms of clinical presentation, extent and severity of organ involvement, immunologic abnormalities, and clinical course. Although clinical outcomes appear to have improved in recent years, the disease continues to cause substantial excess mortality. In this review, we have systematically collected the published studies addressing the mortality burden in patients with scleroderma in comparison with the general population, as well as studies exploring the most important potential predictors of mortality. Results of these studies are presented and discussed, with emphasis on methodological limitations. Suggestions are made for the design, conduct, and reporting of further research on these themes.

Abstract

Newly discovered true (non-null) associations often have inflated effects compared with the true effect sizes. I discuss here the main reasons for this inflation. First, theoretical considerations prove that when true discovery is claimed based on crossing a threshold of statistical significance and the discovery study is underpowered, the observed effects are expected to be inflated. This has been demonstrated in various fields ranging from early stopped clinical trials to genome-wide associations. Second, flexible analyses coupled with selective reporting may inflate the published discovered effects. The vibration ratio (the ratio of the largest vs. smallest effect on the same association approached with different analytic choices) can be very large. Third, effects may be inflated at the stage of interpretation due to diverse conflicts of interest. Discovered effects are not always inflated, and under some circumstances may be deflated-for example, in the setting of late discovery of associations in sequentially accumulated overpowered evidence, in some types of misclassification from measurement error, and in conflicts causing reverse biases. Finally, I discuss potential approaches to this problem. These include being cautious about newly discovered effect sizes, considering some rational down-adjustment, using analytical methods that correct for the anticipated inflation, ignoring the magnitude of the effect (if not necessary), conducting large studies in the discovery phase, using strict protocols for analyses, pursuing complete and transparent reporting of all results, placing emphasis on replication, and being fair with interpretation of results.

Abstract

The increased use of meta-analysis in systematic reviews of healthcare interventions has highlighted several types of bias that can arise during the completion of a randomised controlled trial. Study publication bias has been recognised as a potential threat to the validity of meta-analysis and can make the readily available evidence unreliable for decision making. Until recently, outcome reporting bias has received less attention.We review and summarise the evidence from a series of cohort studies that have assessed study publication bias and outcome reporting bias in randomised controlled trials. Sixteen studies were eligible of which only two followed the cohort all the way through from protocol approval to information regarding publication of outcomes. Eleven of the studies investigated study publication bias and five investigated outcome reporting bias. Three studies have found that statistically significant outcomes had a higher odds of being fully reported compared to non-significant outcomes (range of odds ratios: 2.2 to 4.7). In comparing trial publications to protocols, we found that 40-62% of studies had at least one primary outcome that was changed, introduced, or omitted. We decided not to undertake meta-analysis due to the differences between studies.Recent work provides direct empirical evidence for the existence of study publication bias and outcome reporting bias. There is strong evidence of an association between significant results and publication; studies that report positive or significant results are more likely to be published and outcomes that are statistically significant have higher odds of being fully reported. Publications have been found to be inconsistent with their protocols. Researchers need to be aware of the problems of both types of bias and efforts should be concentrated on improving the reporting of trials.

Abstract

The author evaluated the implications of nominal statistical significance for changing the credibility of null versus alternative hypotheses across a large number of observational associations for which formal statistical significance (p < 0.05) was claimed. Calculation of the Bayes factor (B) under different assumptions was performed on 272 observational associations published in 2004-2005 and a data set of 50 meta-analyses on gene-disease associations (752 studies) for which statistically significant associations had been claimed (p < 0.05). Depending on the formulation of the prior, statistically significant results offered less than strong support to the credibility (B > 0.10) for 54-77% of the 272 epidemiologic associations for diverse risk factors and 44-70% of the 50 associations from genetic meta-analyses. Sometimes nominally statistically significant results even decreased the credibility of the probed association in comparison with what was thought before the study was conducted. Five of six meta-analyses with less than substantial support (B > 0.032) lost their nominal statistical significance in a subsequent (more recent) meta-analysis, while this did not occur in any of seven meta-analyses with decisive support (B < 0.01). In these large data sets of observational associations, formal statistical significance alone failed to increase much the credibility of many postulated associations. Bayes factors may be used routinely to interpret "significant" associations.

Abstract

Appraisal of the scientific impact of researchers, teams and institutions with productivity and citation metrics has major repercussions. Funding and promotion of individuals and survival of teams and institutions depend on publications and citations. In this competitive environment, the number of authors per paper is increasing and apparently some co-authors don't satisfy authorship criteria. Listing of individual contributions is still sporadic and also open to manipulation. Metrics are needed to measure the networking intensity for a single scientist or group of scientists accounting for patterns of co-authorship. Here, I define I(1) for a single scientist as the number of authors who appear in at least I(1) papers of the specific scientist. For a group of scientists or institution, I(n) is defined as the number of authors who appear in at least I(n) papers that bear the affiliation of the group or institution. I(1) depends on the number of papers authored N(p). The power exponent R of the relationship between I(1) and N(p) categorizes scientists as solitary (R>2.5), nuclear (R = 2.25-2.5), networked (R = 2-2.25), extensively networked (R = 1.75-2) or collaborators (R<1.75). R may be used to adjust for co-authorship networking the citation impact of a scientist. I(n) similarly provides a simple measure of the effective networking size to adjust the citation impact of groups or institutions. Empirical data are provided for single scientists and institutions for the proposed metrics. Cautious adoption of adjustments for co-authorship and networking in scientific appraisals may offer incentives for more accountable co-authorship behaviour in published articles.

Abstract

In an effort to pinpoint potential genetic risk factors for schizophrenia, research groups worldwide have published over 1,000 genetic association studies with largely inconsistent results. To facilitate the interpretation of these findings, we have created a regularly updated online database of all published genetic association studies for schizophrenia ('SzGene'). For all polymorphisms having genotype data available in at least four independent case-control samples, we systematically carried out random-effects meta-analyses using allelic contrasts. Across 118 meta-analyses, a total of 24 genetic variants in 16 different genes (APOE, COMT, DAO, DRD1, DRD2, DRD4, DTNBP1, GABRB2, GRIN2B, HP, IL1B, MTHFR, PLXNA2, SLC6A4, TP53 and TPH1) showed nominally significant effects with average summary odds ratios of approximately 1.23. Seven of these variants had not been previously meta-analyzed. According to recently proposed criteria for the assessment of cumulative evidence in genetic association studies, four of the significant results can be characterized as showing 'strong' epidemiological credibility. Our project represents the first comprehensive online resource for systematically synthesized and graded evidence of genetic association studies in schizophrenia. As such, it could serve as a model for field synopses of genetic associations in other common and genetically complex disorders.

Abstract

Rare cardiovascular events of commonly used drugs are important to document and investigate, but single trials are notoriously underpowered to provide conclusive evidence. Recently, meta-analyses have been used to improve on the power. A recent rosiglitazone meta-analysis heightened the debate about the usefulness and limitations of meta-analysis in this setting. In this review, we examined the methods used in previous published meta-analyses for harmful cardiovascular events, with special attention to the rosiglitazone meta-analyses, and give suggestions for the improvement of methods and interpretation of such meta-analyses. The conduct of meta-analysis in this context is particularly difficult and requires timely investigation, availability of high-quality data on harms, and statistical expertise. There are important decisions that need to be made about selecting the appropriate analytical methods and performing sensitivity analyses to evaluate whether the results are robust to different analytical choices.

Abstract

In the interpretation of research evidence, data that have been accumulated in a specific isolated study are typically examined. However, important biases may precede the study design. A study may be misleading, useless, or even harmful, even though it seems to be perfectly designed, conducted, analyzed, and reported. Some biases pertain to setting the wider research agenda and include poor scientific relevance, minimal clinical utility, or failure to consider prior evidence (non-consideration of prior evidence, biased consideration of prior evidence, or consideration of biased prior evidence). Other biases reflect issues in setting the specific research questions: examples include straw man effects, avoidance of head-to-head comparisons, head-to-head comparisons bypassing demonstration of effectiveness, overpowered studies, unilateral aims (focusing on benefits and neglecting harms), and the approach of the industry towards research as bulk advertisement (including ghost management of the literature). The concerted presence of such biases may have a multiplicative, detrimental impact on the scientific literature. These issues should be considered carefully when interpreting research results.

Abstract

To examine whether doctors' global assessments of treatment effects agree with patients' global assessments.Survey of trials included in systematic reviews of treatments for diverse conditions.Cochrane database of systematic reviews. Data extracted Data on patients' global assessments and on doctors' global assessment for the same treatment against the same comparator.Relative odds ratio (ratio of odds ratios of global improvement with the experimental intervention versus control according to doctors compared with patients), and improvement rates according to doctors and patients.Doctors' global assessments were compared with patients' global assessments for 63 different treatment comparisons (240 trials) in 18 conditions. The summary relative odds ratio across the comparisons was not significant (0.98, 95% confidence interval 0.88 to 1.08; I(2)=0%, 95% confidence interval 0% to 30%). In 62 of the 63 comparisons the effects of treatment rated by patients and by doctors did not differ beyond chance, but for single comparisons the confidence intervals were large. Rates of improvement on average did not differ between doctors' assessments and patients' assessments (summary relative odds ratio 0.98, 0.88 to 1.06; I(2)=0%, 0% to 24%).Doctors' global assessments of the effects of treatments are on average similar to those of patients.

Abstract

Randomized trials may be designed and interpreted as single experiments or they may be seen in the context of other similar or relevant evidence. The amount and complexity of available randomized evidence vary for different topics. Systematic reviews may be useful in identifying gaps in the existing randomized evidence, pointing to discrepancies between trials, and planning future trials. A new, promising, but also very much debated extension of systematic reviews, mixed treatment comparison (MTC) meta-analysis, has become increasingly popular recently. MTC meta-analysis may have value in interpreting the available randomized evidence from networks of trials and can rank many different treatments, going beyond focusing on simple pairwise-comparisons. Nevertheless, the evaluation of networks also presents special challenges and caveats. In this article, we review the statistical methodology for MTC meta-analysis. We discuss the concept of inconsistency and methods that have been proposed to evaluate it as well as the methodological gaps that remain. We introduce the concepts of network geometry and asymmetry, and propose metrics for the evaluation of the asymmetry. Finally, we discuss the implications of inconsistency, network geometry and asymmetry in informing the planning of future trials.

Inflated numbers of authors over time have not been just due to increasing research complexityJOURNAL OF CLINICAL EPIDEMIOLOGYPapatheodorou, S. I., Trikalinos, T. A., Ioannidis, J. P.2008; 61 (6): 546-551

Abstract

To examine trends in and determinants of the number of authors in clinical studies.We analyzed determinants of the number of authors in 633 articles of randomized trials and 313 articles of nonrandomized studies included in large meta-analyses (seven and six topics, respectively). Analyses were adjusted for topic. We also evaluated 310 randomly sampled case reports that had an abstract and described a single case.After adjusting for topic and other determinants, for both randomized trials and nonrandomized studies, the number of authors increased by 0.8 per decade (P<0.001). Topic was a strong determinant of the number of authors; other independent factors included journal impact factor, multinational authorship, and (for randomized trials) article length and sample size. Trials from South Europe (+1.1 authors) and North America (+0.9) and nonrandomized studies from South Europe (+1.8) had more authors than studies from North Europe (P<0.001). For case reports, only geographic location and article length were significantly related with author numbers.The number of authors in articles of randomized and nonrandomized studies has increased over time, even after adjusting for the topic, size, and visibility of a study. The academic coinage of authorship may be suffering from inflation.